A Framework for Remote Patient Monitoring to Diagnose the Cardiac Disorders

A Framework for Remote Patient Monitoring to Diagnose the Cardiac Disorders
A Framework for Remote Patient Monitoring
to Diagnose the Cardiac Disorders
Thesis submitted to the
National Institute of Technology Rourkela
in partial fulfillment of the requirements for the degree
of
Master of Technology (Research)
GOUTAM KUMAR SAHOO
Roll No: 611EC601
Department of Electronics and Communication Engineering
National Institute of Technology Rourkela
Rourkela, Odisha 769 008
INDIA
2015
A Framework for Remote Patient Monitoring
to Diagnose the Cardiac Disorders
Thesis submitted to the
National Institute of Technology Rourkela
in partial fulfillment of the requirements for the degree
of
Master of Technology (Research)
by
Goutam Kumar Sahoo
(Roll No: 611EC601)
under the guidance of
Dr. Samit Ari
&
Prof. (Dr.) Sarat Kumar Patra
Department of Electronics and Communication Engineering
National Institute of Technology Rourkela
Rourkela, Odisha 769 008
INDIA
Dedicated to my Parents
i
Dept. of Electronics & Communication Engineering
National Institute of Technology Rourkela
Rourkela, Odisha 769 008, INDIA
30 July 2015
Certificate
This is to certify that the work in the thesis entitled “A Framework for Remote
Patient Monitoring to Diagnose the Cardiac Disorders" by Goutam Kumar
Sahoo is a record of his original research work carried out under our supervision and
guidance. He has fulfilled all prescribed requirements for the award of the degree of
Master of Technology (R) in Electronics and Communication Engineering. Neither this
thesis nor any part of this has been submitted for any degree or academic award elsewhere.
Dr. Sarat Kumar Patra
(Co-Supervisor)
Professor, ECE Department
NIT Rourkela, Odisha
Dr. Samit Ari
(Supervisor)
Asst. Professor, ECE Department
NIT Rourkela, Odisha
ACKNOWLEDGEMENTS
It has been a great experience to work under esteemed supervision of Dr. Samit Ari
and Dr. Sarat Kumar Patra. I am very much privileged to have them as my research
guides. I would like to thank them from the bottom of my heart for their involvement,
guidance, most importantly their support and encouragement throughout the project
work. I would also like to thank for their suggestions and comments.
I would like to thank my MSC members Prof. K. K. Mahapatra, Prof. P. K. Sahu
and Prof. D. Patra for their suggestions and help in due course of project work. I would
like to thank Prof. S. Meher, Prof. S. K. Behera, Prof. S. K. Das, Prof. A. K. Swain,
Prof. L. P. Roy, and Prof. S. M. Hiremath for inspiring me in many ways. I am also
thankful to other faculties and staffs of Electronics and Communication Engineering
department for their support.
I would like to mention the names of Prasanta Pradhan, Manab Das, Dipak Ghosh,
Pallab Maji, Manas Biswal and all other members of Pattern recognition Lab, and
advance communication Lab, for their constant support and co-operation throughout
the course of the project. I would also like to thank all my friends within and outside
the department for all their encouragement, motivation and the experiences that they
shared with me.
Finally, I would like to thank my parents and my elder brother who have given me good
moral support and encouragement throughout my study at NIT Rourkela.
Date:
Goutam Kumar Sahoo
iii
ABBREVIATIONS
AF
ANN
Atrial Fibrillation
Artificial Neural Network
AT
ATtention
AV
Atrioventricular
aVF
Augmented Vector Foot
aVL
Augmented Vector Left
aVR
Augmented Vector Right
AWES
AZTEC
BIH
Ambulatory Wireless ECG Sensor
Amplitude Zone Time Epoch Coding
Beth Israel Hospital (now Beth Israel Deaconess Medical Center)
BPM
Bits Per Minute
CHD
Coronary Heart Disease
CVD
Cardio Vascular Disease
CORTES
CR
DCT
DPCM
DVD-ROMs
Coordinate Reduction Time Encoding System
Compression Ratio
Discrete Cosine Transform
Differential Pulse Code Modulation
Digital Versatile Disc-Read Only Memories
DWT
Discrete Wavelet Transform
ECG
Electrocardiogram
EMD
Empirical Mode Decomposition
iv
ESC
FN
FOI-2DF
FP
FWT
GB
GPRS
GSM
European Society of Cardiology
False Negative
First-Order Interpolation with two Degrees of Freedom
False Positive
Fast Wavelet Transform
Gigabyte
General Packet Radio Service
Global System for Mobile communications
HR
Heart Rate
HT
Hilbert Transform
HTT
IDCT
IF
Hilbert Huang Transform
Inverse Discrete Cosine Transform
Instantaneous Frequency
IHD
Ischemic Heart Disease
IMF
Intrinsic Mode Function
JPEG
KLT
LA
LADT
LL
MATLAB
MB
MI
MIT
MMS
Joint Photographic Expert Group
Karhunen-Loeve Transform
Left Arm
Linear Approximation Distance Thresholding
Left Leg
Matrix Laboratory
Megabyte
Myocardial Infarction
Massachusetts Institute of Technology
Multimedia Messaging Service
NN
Neural Network
PC
Personal Computer
PCA
Principal Component Analysis
v
PDA
Personal Digital Assistant
PDU
Protocol Data Unit
PPA
Positive Predictive Accuracy
PRD
Percent Root Mean Square Difference
PSVT
RA
RAM
RECAD
SA
SAPA
SD
Se
Paroxysmal Supraventricular Tachycardia
Right Arm
Random-Access Memory
Real-time Continuous Arrhythmias Detection
Sinoatrial
Scan-Along Polygonal Approximation
Standard Deviation
Sensitivity
SIM
Subscriber Identity Module
SMS
Short Message Service
SMSC
SNR
Sp
SPIHT
TDM
TIA
SMS Center
Signal-to-Noise Ratio
Specificity
Set Partitioning In Hierarchical Trees
Time Division Multiplexing
Transient Ischemic Attack
TP
True Positive
TP
Turning Point
USB
Universal Serial Bus
WHO
World Health Organization
WPW
Wolff-Parkinson- White
vi
CONTENTS
Certificate
ii
Acknowledgements
iii
Abbreviations
iv
List of Tables
xi
List of Figures
xii
Abstract
xiv
1 Introduction
1
1.1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1
1.2
Human heart anatomy . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3
1.3
Electrocardiogram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4
1.3.1
Basic ECG patterns . . . . . . . . . . . . . . . . . . . . . . . . . .
5
1.3.2
ECG lead placement . . . . . . . . . . . . . . . . . . . . . . . . . .
7
1.4
Cardiovascular disorders . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9
1.5
Cardiac dysrhythmia or heart rhythm abnormality . . . . . . . . . . . . . 10
1.5.1
Causes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
1.5.2
Major risk factors . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
1.5.3
Symptoms of arrhythmias . . . . . . . . . . . . . . . . . . . . . . . 11
vii
CONTENTS
1.5.4
1.6
1.7
Types of arrhythmia . . . . . . . . . . . . . . . . . . . . . . . . . . 12
Cardiac ischemia or Ischemic heart disease . . . . . . . . . . . . . . . . . . 13
1.6.1
Cardiac ischemia categories . . . . . . . . . . . . . . . . . . . . . . 13
1.6.2
Causes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
1.6.3
Major risk factors . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
1.6.4
Different types of ischemia . . . . . . . . . . . . . . . . . . . . . . . 15
ST-segment analysis of ECG . . . . . . . . . . . . . . . . . . . . . . . . . 16
1.7.1
ST-segment elevation
. . . . . . . . . . . . . . . . . . . . . . . . . 18
1.7.2
ST-segment depression . . . . . . . . . . . . . . . . . . . . . . . . . 18
1.7.3
T-wave . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
1.7.4
Isoelectric line . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
1.8
European ST-T ECG database . . . . . . . . . . . . . . . . . . . . . . . . 20
1.9
Mobile health care . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
1.10 GSM modem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
1.11 Literature survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
1.11.1 On ECG signal compression
. . . . . . . . . . . . . . . . . . . . . 24
1.11.2 On ECG data transmission over wireless medium . . . . . . . . . . 27
1.11.3 On detection of cardiac disorders . . . . . . . . . . . . . . . . . . . 28
1.12 Objectives of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
1.13 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
2 ECG Signal Compression and Decompression
33
2.1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
2.2
Types of compression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
2.2.1
Lossless techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
2.2.2
Lossy techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
2.3
Empirical Mode Decomposition . . . . . . . . . . . . . . . . . . . . . . . . 37
2.4
Proposed Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
viii
CONTENTS
2.4.1
Compression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
2.4.2
Decompression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
2.5
Experimental Results and Discussions . . . . . . . . . . . . . . . . . . . . 47
2.6
Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
3 Transmission of compressed ECG using SMS
54
3.1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
3.2
SMS based data transmission . . . . . . . . . . . . . . . . . . . . . . . . . 55
3.3
3.2.1
Computer based SMS transmission . . . . . . . . . . . . . . . . . . 56
3.2.2
Brief introduction on AT-commands . . . . . . . . . . . . . . . . . 57
Proposed Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
3.3.1
Methodology for wireless transmission of compressed ECG . . . . . 58
3.3.2
Methodology for ECG signal reconstruction . . . . . . . . . . . . . 62
3.4
Experimental Results and Discussions . . . . . . . . . . . . . . . . . . . . 64
3.5
Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
4 Detection of cardiac disorders like bradycardia, tachycardia and ischemia 68
4.1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
4.2
Proposed framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
4.2.1
Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
4.2.2
Heart rhythm abnormality detection . . . . . . . . . . . . . . . . . 73
4.2.3
Feature extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
4.2.4
Ischemic beat classification . . . . . . . . . . . . . . . . . . . . . . 75
4.2.5
Ischemic episode recognition . . . . . . . . . . . . . . . . . . . . . . 76
4.3
Experimental Results and Discussions . . . . . . . . . . . . . . . . . . . . 77
4.4
Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
5 Conclusion and Future Scope of Work
80
5.1
Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
5.2
Future Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
ix
CONTENTS
References
84
Publications
98
x
LIST OF TABLES
1.1
Description of different waves and segments in a ECG cycle . . . . . . . . 17
1.2
AT commands used in writing and sending SMS . . . . . . . . . . . . . . 23
1.3
AT commands used in reading and receiving SMS . . . . . . . . . . . . . . 24
2.1
Evaluation of CR and PRD for European ST-T database
2.2
Performance of proposed compression algorithm . . . . . . . . . . . . . . . 50
2.3
Other algorithms comparison with proposed method for ECG record 117.
2.4
Performance comparison of different type ECG compression schemes . . . 51
2.5
Performance comparison of proposed method with Zahhad et al. method
2.6
Performance evaluation of real time ECG signals recorded at 400Hz . . . 52
2.7
Performance evaluation of real time ECG signals recorded at 1000Hz . . . 52
3.1
Evaluation of PRD for European ST-T database . . . . . . . . . . . . . . 65
4.1
Heart rhythm abnormalities identification . . . . . . . . . . . . . . . . . . 78
4.2
Results of ischemic episode detection . . . . . . . . . . . . . . . . . . . . . 78
xi
. . . . . . . . . 48
50
52
LIST OF FIGURES
1.1
Block diagram of wireless tele-cardiology system . . . . . . . . . . . . . .
3
1.2
Cross section of a human heart . . . . . . . . . . . . . . . . . . . . . . . .
4
1.3
(a) Human heart cross sectional view (b) Generation of ECG wave . . . .
6
1.4
One complete ECG cycle
. . . . . . . . . . . . . . . . . . . . . . . . . . .
7
1.5
Positions of 12 ECG leads . . . . . . . . . . . . . . . . . . . . . . . . . . .
8
1.6
ECG graph with different peaks and intervals . . . . . . . . . . . . . . . . 18
1.7
ST-segment elevation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
1.8
ST-segment depression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
1.9
T-wave alternation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
1.10 ECG graph showing isoelectric line . . . . . . . . . . . . . . . . . . . . . . 21
2.1
Work flow diagram for ECG signal compression . . . . . . . . . . . . . . . 41
2.2
Decomposition of input ECG signal (European ST-T record no. # e0613)
2.3
Work flow diagram for ECG signal reconstruction . . . . . . . . . . . . . . 45
2.4
Signals at different stages of ECG signal reconstruction . . . . . . . . . . 48
3.1
Wireless modem connection to a computer
3.2
Setup for wireless ECG transmitter
3.3
Real time work setup for wireless ECG transmission . . . . . . . . . . . . 60
3.4
Setup for ECG signal reconstruction . . . . . . . . . . . . . . . . . . . . . 62
xii
42
. . . . . . . . . . . . . . . . . 57
. . . . . . . . . . . . . . . . . . . . . 59
List of Figures
3.5
Received SMS messages . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
3.6
Flow chart for reconstruction of ECG signal
3.7
The SMS based ECG transmitter output . . . . . . . . . . . . . . . . . . . 65
3.8
Original and reconstructed ECG signal . . . . . . . . . . . . . . . . . . . . 66
3.9
Normalised error square signal in dB . . . . . . . . . . . . . . . . . . . . . 66
4.1
Block diagram for cardiac disorder detection
4.2
Complete flow diagram of proposed framework . . . . . . . . . . . . . . . 71
4.3
Stages of QRS-complex detection . . . . . . . . . . . . . . . . . . . . . . . 72
4.4
Different signals during filtering . . . . . . . . . . . . . . . . . . . . . . . . 73
4.5
RR-interval determination using QRS-complex peaks . . . . . . . . . . . . 74
4.6
ECG signal extracted wave peaks and points with baseline
xiii
. . . . . . . . . . . . . . . . 64
. . . . . . . . . . . . . . . . 70
. . . . . . . . 76
ABSTRACT
Electrocardiogram (ECG) is an efficient diagnostic tool to monitor the electrical activity of heart. One of the most vital benefit of using telecommunication technologies in
medical field is to provide cardiac health care at a distance. Telecardiology is the most
efficient way to provide faster and affordable health care for the cardiac patients located
at rural areas. Early detection of cardiac disorders can minimize cardiac death rates. In
real time monitoring process, ECG data from a patient usually takes large storage space
in the order of gigabytes (GB). Hence, compression of bulky ECG signal is a common
requirement for faster transmission of cardiac signals using wireless technologies. Several techniques such as the Fourier transform based methods, wavelet transform based
methods, etc., have been reported for compression of ECG data. Though Fourier transform is suitable for analyzing the stationary signals. An improved version, the wavelet
transform allows the analysis of non-stationary signal. It provides a uniform resolution
for all the scales, however, wavelet transform faces difficulties like uniformly poor resolution due to limited size of the basic wavelet function and it is nonadaptive in nature.
A data adaptive method to analyse non-stationary signal is based on empirical mode
decomposition (EMD), where the bases are derived from the multivariate data which
are nonlinear and non-stationary. A new ECG signal compression technique based on
EMD is proposed, in which first EMD technique is applied to decompose the ECG signal
into several intrinsic mode functions (IMFs). Next, downsampling, discrete cosine transform (DCT), window filtering and Huffman encoding processes are used sequentially to
compress the ECG signal. The compressed ECG is then transmitted as short message
xiv
List of Figures
service (SMS) message using a global system for mobile communications (GSM) modem.
First the AT-command ‘+CMGF’ is used to set the SMS to text mode. Next, the GSM
modem uses the AT-command ‘+CMGS’ to send a SMS message. The received text
SMS messages are transferred to a personal computer (PC) using blue-tooth. All text
SMS messages are combined in PC as per the received sequence and fed as data input
to decompress the compressed ECG data. The decompression method which is used to
reconstruct the original ECG signal consists of Huffman decoding, inverse discrete cosine
transform (IDCT) and spline interpolation. The performance of the compression and
decompression techniques are evaluated in terms of compression ratio (CR) and percent
root mean square difference (PRD) respectively by using both European ST-T database
and Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia
database. The average values of CR and PRD for selected ECG records of European
ST-T database are found to be 23.5:1 and 1.38 respectively. All 48 ECG records of
MIT-BIH arrhythmia database are used for comparison purpose and the average values of CR and PRD are found to be 23.74:1 and 1.49 respectively. The reconstructed
ECG signal is then used for detection of cardiac disorders like bradycardia, tachycardia
and ischemia. The preprocessing stage of the detection technique filters the normalized
signal to reduce noise components and detects the QRS-complexes. Next, ECG feature extraction, ischemic beat classification and ischemic episode detection processes are
applied sequentially to the filtered ECG by using rule based medical knowledge. The
ST-segment and T-wave are the two features generally used for ischemic beat classification. As per the recommendation of ESC (European Society of cardiology) the ischemic
episode detection procedure considers minimum 30s duration of signal. The performance
of the ischemic episode detection technique is evaluated in terms of sensitivity (Se) and
positive predictive accuracy (PPA) by using European ST-T database. This technique
achieves an average Se and PPA of 83.08% and 92.42% respectively.
xv
CHAPTER 1
INTRODUCTION
1.1
Introduction
Current technological
advance in medical science has made life better for everyone.
Use of wireless technology in medical science provides faster and essential health care.
Presently, heart diseases are the most common cause of human death. Abnormality in
functioning of heart produces heart diseases. Early detection of heart disorders can offer
long life to the heart patients. Electrocardiogram (ECG) is an efficient diagnostic tool to
monitor the electrical activity of heart [1]. ECG provides valuable diagnostic information
about functioning of heart and cardiovascular system. A patient located at a rural area
faces lots of difficulties in getting appropriate treatment due to unavailability of advance
equipments and specialist doctors. In this scenario mobile health care and remote health
care system is helpful.
Remote health monitoring is a new model of health care service to monitor a patient
at home using wireless communication network [2]. In this type of health care, a patient
or physician located in a remote location can communicate and discuss with doctors
located at a distance [3]. If necessary patient can send the required data immediately
to the expert doctors. The purpose of remote health care system is to provide faster
and valuable diagnosis to a remote patient. In remote patient monitoring, unnecessary
1
Chapter 1. Introduction
travelling cost and time delay to get proper health care is minimized [4]. Here patient
gets proper health care at home from expert doctors without moving from one hospital
to another. Hence, remote monitoring offers improved patient safety and quality of
health care. In real time cardiac patient monitoring, ECG data from a patient usually
takes large storage space in the order of gigabytes (GB) [5]. It is very difficult to store
and transfer this bulky ECG signal. Hence, this large amount of ECG data need to be
compressed for establishing faster diagnosis through wireless medium. Compression of
bulky ECG signal for minimal memory usage and better storage efficiency is an important
aspect of signal processing. Moreover, for remote health care systems, the ECG signal is
to be compressed sufficient enough so that, it can be transmitted over wireless medium.
Faster data transmission from a patient to a physician or doctor can be possible
using wireless technology. In this thesis, a low cost and efficient method based on short
message service (SMS) is used to transmit the compressed ECG. The received compressed
ECG data are decompressed to reconstruct the ECG signal for immediate diagnosis by
experienced physicians or expert doctors. An efficient and faster detection of cardiac
disorders are necessary to take preliminary action towards the treatment. One of the
important parameter to evaluate a person’s health is the heart rate (HR). A healthy
person’s HR depends on normal rhythm of heart. The heart rhythm defines the speed
of the heartbeat. The heart rate is the number of heartbeats per unit of time. Generally
HR is measured in beats per minute (bpm). The abnormal heart rhythm causes slow or
fast heartbeat. The cardiac disorder, ischemia or heart stroke, affects the heart and the
blood vessels. Ischemia occurs due to inadequate blood flow and oxygen to a particular
part of the body. It can occur in the limbs, heart, brain, or intestines. The ECG beat
classification is essential for automatic detection and diagnosis of heart stroke. ECG
Consist of PQRST waveform. The key to detect ischemia is the measurement of STsegment deviation and change in T-wave amplitude. The abnormal heart rhythms are
identified by calculating HR and the performance of the ischemia detection technique
is evaluated in terms of sensitivity (Se) and positive predictive accuracy (PPA). The
2
Chapter 1. Introduction
standard ECG data from European ST-T database [6] is used for experimental testing
purpose and Massachusetts Institute of Technology-Berth Israel Hospital (MIT-BIH)
arrhythmia database [7] is used for comparison purpose.
A wireless tele-cardiology system for transmission of compressed ECG and detection
of the cardiac disorders is shown in Figure 1.1. First, ECG signal is compressed using
EMD based technique. Next, a GSM modem is used to transmit the compressed ECG
as SMS. The received SMS messages are decompressed to reconstruct the original signal.
The reconstructed ECG is then analysed to detect the cardiac disorders. Most of the
cardiac or heart disorders occurs due to the restriction of blood flow and oxygen to the
heart. Detail functioning of heart, electrical conduction of the heart and the cardiac
cycle are described in following sections.
SMS
ECG Signal
ECG Signal
Compression
GSM Modem
or
Mobile Phone
Mobile Phone
or
GSM Modem
ECG Signal
Reconstruction
Detection of
Cardiac disorders
Figure 1.1: Block diagram of wireless tele-cardiology system
1.2
Human heart anatomy
Heart supplies blood throughout the body. It is located between the lungs in the left
side of the sternum. A cross sectional view of human heart is shown in Figure 1.2. The
heart is made up of four chambers. The upper two chambers are called the left and right
atria, while the lower two chambers are called the left and right ventricles [1].
• Right atrium: This chamber consists of de-oxygenated blood, that returns from
the body, this de-oxygenated blood is then passed on to the right ventricle.
• Right ventricle: It is a chamber, that consists of de-oxygenated blood which is
passed into the lungs for oxygenation.
3
Chapter 1. Introduction
• Left atrium: This is the chamber, where the oxygenated blood enters from the
pulmonary vein. The blood from the left atrium is then forced into the left ventricle.
• Left ventricle: The oxygenated blood enters the left ventricle and is then forced
from the left ventricle into the aorta. The aorta carries the oxygenated blood from
the heart to the other parts of the body.
Left common carotid artery
Brachiocephalic artery
Superior vena cava
Right pulmonary arteries
Right pulmonary veins
Left subclavian artery
Aorta
Left pulmonary arteries
Right atrium
Left pulmonary veins
Left atrium
Semilunar valves
Atrioventricular valve
Atrioventricular valve
Chordae tendineae
Right ventricle
Left ventricle
Septum
Inferior vena cava
Figure 1.2: Cross section of a human heart
The heart is made of cardiac muscle tissue, that contracts and relaxes throughout the
lifetime of a person and this contraction and relaxation of the muscle drives the blood
from the heart. The contraction and relaxation of the cardiac muscle is in a rhythm,
when the cardiac muscle of the heart’s ventricles contract, it is called as systole and
when the cardiac muscle of heart’s ventricles relax, it is called as diastole [1].
1.3
Electrocardiogram
The electrocardiogram (ECG) has become an essential part for complete medical evaluation on any type of cardiac disease diagnostic test. The ECG waveform allows information about electrical activity associated with different aspects of the heartbeat.
The manner in which the heart contracts over time determines the rhythm of the heart.
4
Chapter 1. Introduction
A normal cardiac rhythm is referred to as a ‘sinus’ rhythm. Normal sinus rhythm is
characterized, that there is no disease or disorder affecting heart. Deviation from this
normal sinus rhythm is known as cardiac arrhythmias. The abnormal rhythm can be
life threatening. If the heart rate is too slow then pumping of blood to the blood vessels
may be insufficient, which affects vital organs else for fast rate, the ventricles are not
completely filled before contraction and pumping efficiency drops. Therefore any change
in heart rhythm caused by cardiac arrhythmias will reflect in the person’s ECG [1]. In
General, ECG provides following information [8].
• Position of the heart and the size of the chambers.
• Origin of impulse and its propagation.
• Heart rate and disturbances in conduction.
• Variations in electrolyte concentrations.
• Position of myocardial ischemia.
1.3.1
Basic ECG patterns
ECG shows the electrical activity of the heart. The electrical activity of heart is measured
by placing electrodes on the skin of a patient. The wave of electrical activity spreads
from the atria to the ventricles. Generation of ECG wave corresponds to a specific part
of the heart is shown in the Figure 1.3. The paths of electrical activities are recorded
for determination of heart rhythm abnormalities. The waves and segments of the ECG
are described as follows.
• P-wave: It comes first and represents the depolarization of atria. During this time
the electrical impulse starts from SA (sinoatrial) node to AV (atrioventricular) node
spreading through both the atria [1].
• QRS-complex: This represents the depolarization of ventricles and is the strongest
wave in ECG. QRS-complex consists of three peaks: ‘Q’ and ‘S’ are negative peaks
and ‘R’ is the positive peak [9].
5
Chapter 1. Introduction
Action Potentials
S-A Node
Atrial Muscle
A-V Node
Common Bundle
Bundle Branches
Purkinje Fibres
Ventricular Muscle
P
(a)
T
(b)
QRS
Figure 1.3: (a) Human heart cross sectional view (b) Generation of ECG wave
• PR-interval: The delay between P-wave and QRS-complex. During this time, the
electrical impulse travels from the atria to the ventricles through the AV node [10].
• T-wave: This is a positive defection soon after the QRS-complex and represents
repolarization of the ventricles [8].
• ST-segment: This is the time duration between S-wave and the outset of T-wave
and occurs between the depolarization and repolarization of ventricles. ST-segment
always align with the isoelectric line [10].
• U-wave: It is a small deflection following T-wave and represents the repolarization
of purkinje fibres [10].
A typical ECG wave for one cardiac cycle is shown in the Figure 1.4. Generally, one cycle
ECG signal consists of mainly three features i.e. P-wave, QRS-complex, T-wave and Uwave, which is visible sometimes. The baseline (isoelectric line) is the flat horizontal
segments of ECG. The baseline is measured as a portion of the tracing following the
T-wave to the next P-wave and the PR-segment.
6
Chapter 1. Introduction
QRS complex
R
PR segment
ST segment
T
P
U
Q
Isoelectric
line
S
PR - interval
QT - interval
Figure 1.4: One complete ECG cycle
1.3.2
ECG lead placement
A standard clinical ECG consists of 12 different vectors known as “leads". A lead is
a particular view of the electrical activity of the heart. The electrical potential are
obtained by a pair of electrodes placed at different locations on the body surface and
generates different ECG vectors. Figure 1.5 represents the positions of 12 ECG leads.
Six leads out of these 12 leads are in the plane parallel to the body and other six ECG
leads are views of the heart in the plane perpendicular to the body. A standard 12-lead
ECG consist of three bipolar limb leads, three unipolar limb leads and six chest leads.
1.3.2.1
Bipolar Limb Leads
Leads I, II and III belongs to this category. These leads are obtained with electrodes of
opposite polarity (+ve and -ve) [10].
• Lead I: Difference between left arm (LA) electrode potential and right arm (RA)
electrode potential (LA-RA).
• Lead II: Difference between left leg (LL) electrode potential and right arm (RA)
electrode potential (LL-RA).
7
Chapter 1. Introduction
Figure 1.5: Positions of 12 ECG leads
• Lead III: Difference between left leg (LL) electrode potential and left arm(LA)
electrode potential (LL-LA).
1.3.2.2
Unipolar Limb Leads
Augmented vector right (aVR), augmented vector left (aVL) and augmented vector foot
(aVF) are the unipolar limb leads. These leads are obtained with a single positive
electrode and a reference point which are lies in the center of heart’s electric field [10].
These leads are explained as follows.
• aVR: The potential difference between right arm electrode and the center of heart’s
electric field.
• aVL: The potential difference between left arm electrode and the center of heart’s
electric field.
• aVF: The potential difference between left leg and the center of the heart’s electric
field.
8
Chapter 1. Introduction
1.3.2.3
Unipolar chest Leads
Leads V 1 − V 6 are unipolar chest leads. Here, the positive electrodes of leads V 1 − V 6
is placed at specific points on the chest as shown in the Figure 1.5. The leads show the
potential difference between the positive electrode and the center of the heart’s electric
field [10]. The locations of the positive electrodes for V 1 − V 6 leads are given below.
• V 1 : Fourth intercostal space in right side of sternum.
• V 2 : Fourth intercostal space in left side of sternum.
• V 3 : Directly between V 2 and V 4 .
• V 4 :Fifth Intercostal space on the left mid-clavicular line.
• V 5 : In the same level of V 4 at anterior axillary line on the left side.
• V 6 : In the same level of V 5 at mid-axillary line on the left side.
1.4
Cardiovascular disorders
Heart disease is one of the major cause of death in humans. Inadequate supply of
blood and oxygen is one of the most common cause of all heart diseases. World health
organization (WHO) in 2008 estimated death of 17.3 million people from cardiovascular
diseases (CVD) which represent 30% of all global deaths [11]. Out of all CVD deaths
ischemic heart disease (IHD) itself reports 7,249,000 deaths which is 12.7% of total global
mortality. Of these IHD deaths, an estimated 7.3 million are due to coronary heart
disease and 6.2 million are due to stroke [12]. It is anticipated that by the year 2030,
23.6 million people will die from CVDs. Also it is expected that the worst affected region
will be south-east Asia. One of the problems is the poor doctor to patient ratio in the
underdeveloped and developing nations. The doctor to patient ratio in India is as low as
60 per one lakh population as compared to more than 250 per one lakh in the developed
countries [13]. Sometimes the symptoms for heart disease are difficult to detect and it
is not detected until a major issue like heart attack occurs. Sometimes, symptoms are
noticeable like chest pain (angina), extreme fatigue and shortness of breath. Several
categories of heart disease [14] are given as follows.
9
Chapter 1. Introduction
• Coronary heart disease: It is a condition, in which supply of blood and oxygen
to the heart reduced due to formation of plaque in the coronary blood vessels. It
is also known as coronary artery disease.
• Angina pectoris: It is a medical term for chest pain and it occurs due to insufficient supply of blood to the heart.
• Congenital Heart Disease: Commonly known as heart failure. It is a condition,
where the heart cannot pump enough blood to the rest of the body.
• Arrhythmias: It is a disorder in the rhythmic movement of the heartbeat. The
heartbeat can be slow, fast, or irregular.
• Cardiomyopathy: It is the condition of weakening the heart muscle or a change
in the structure of the muscle due to inadequate heart pumping.
• Ischemic Heart Disease: It is a type of coronary artery disease and it results due
to reduced blood supply to the heart. Main cause of this disease is atherosclerosis.
1.5
Cardiac dysrhythmia or heart rhythm abnormality
Cardiac dysrhythmia or heart rhythm abnormality is the conditions in which the electrical activity of the heart is irregular [15]. One study by Hsia et al. analyzed digitized
ECG data in a beat-by-beat mode [16]. Each beat is assigned a beat code based on a
combination of waveform analysis and RR-interval measurement for abnormal rhythm
analysis. The regular or normal heart rhythm is 60 to 100 beats per minute (bpm) [1].
As stated in [15], a heartbeat that is too slow is called bradycardia and a heartbeat that
is too fast is called tachycardia. Heart can not pump enough blood to the body if the
heart rate is irregular. Lack of blood flow can damage the brain, heart and other vital
organs. The heart’s electrical system controls the rate and rhythm of the heartbeat.
1.5.1
Causes
An arrhythmia occurs, if the electrical signals that control the heartbeat are delayed or
blocked. This happens, when electrical signals do not travel normally through the heart.
10
Chapter 1. Introduction
There are various causes of arrhythmia, which are mentioned below.
• Smoking.
• Heavy use of alcohol.
• Use of certain drugs (such as cocaine or amphetamines).
• Too much use of caffeine or nicotine.
• Strong emotional stress or anger leading to raised blood pressure.
1.5.2
Major risk factors
Arrhythmias are more common in people having diseases or different conditions, that
weaken the heart. Various risk factors of arrhythmia are mentioned below.
• Heart attack.
• Heart failure or cardiomyopathy.
• Too thick or strong heart tissue.
• Narrow heart valves.
• Congenital heart defects.
• High blood pressure.
• Diabetes.
1.5.3
Symptoms of arrhythmias
Many arrhythmias has no signs or symptoms, whereas some typical common symptoms
present includes the following.
• Too hard or fast beating of heart.
• Slow heartbeat.
• An irregular heartbeat.
• Feeling pauses between heartbeats.
• Weakness, dizziness and sweating.
• Shortness of breath.
• Chest pain.
11
Chapter 1. Introduction
1.5.4
Types of arrhythmia
Most arrhythmias are harmless, whereas some arrhythmia depends on its severity. People
having arrhythmias can live normal and healthy lives. Four main types of arrhythmia
are reported as follows.
1.5.4.1
Premature (extra) beats
These are most common type of arrhythmias and most of the time, these are harmless.
A person usually feels like wavering in the chest or a feeling of a skipped beat. Most of
the time, premature beats need no treatment, especially in healthy people.
1.5.4.2
Supraventricular arrhythmias
These type of arrhythmias are tachycardias (fast heart rates), that start in the atria
or the atrioventricular (AV) node. Types of supraventricular arrhythmias are atrial
fibrillation (AF), atrial flutter, paroxysmal supraventricular tachycardia (PSVT) and
wolff-parkinson-white (WPW) syndrome, which are explained as follows.
• During AF, atria are not able to pump blood into the ventricles as the walls of
the atria vibrates very fast (fibrillate) instead of beating normally. This spreads
electrical signals in a fast and irregular rhythm through the atria.
• During atrial flutter, electrical signals travel in a fast and regular rhythm. Atrial
flutter causes and symptoms are similar to AF.
• During PSVT, heart rate is a very fast that begins and ends suddenly. It causes
extra heartbeats, which happens during vigorous exercise.
• Wolff-parkinson-white syndrome is a type of PSVT, in which the heart’s electrical signals travel along an extra pathway from the atria to the ventricles. This
extra pathway disrupts the timing of the heart’s electrical signals and causes the
ventricles to beat very fast. This type of arrhythmia can be life threatening.
12
Chapter 1. Introduction
1.5.4.3
Ventricular arrhythmias
These arrhythmias start in the ventricles and usually need medical attention. Ventricular
arrhythmias include ventricular tachycardia and ventricular fibrillation (v-fib). These
are explained below.
• Ventricular tachycardia is a fast, regular beating of the ventricles, that may last
for only a few seconds. An episodes, that last for more than a few seconds can be
dangerous.
• V-fib occurs, when disorganized electrical signals make the ventricles vibrate instead of pump normally. This is dangerous as ventricles unable to pumping blood
out of the body. A person may lose consciousness within seconds and die within
minutes if not treated. This happen during or after a heart attack.
1.5.4.4
Bradyarrhythmias
In these arrythmia the heart rate is much slower than normal. This slow heart rate can
not supply required amount of blood to brain. This situation leads loss of consciousness.
In bradyarrhythmia disease, the heart rate is usually less than 60bpm for adults.
1.6
Cardiac ischemia or Ischemic heart disease
Ischemic heart disease is the most common type of heart disease and a cause of heart
attacks [14]. The disease is caused by plaque building up along the inner walls of the
arteries of the heart, which narrows the arteries and reduces blood flow to the heart.
Ischemia is a condition of relative shortage of oxygen and other nutrients in the supplied
blood to any organ, that damages the tissue. Ischemia may occur in body parts including
the limbs, heart, brain or intestines.
1.6.1
Cardiac ischemia categories
Cardiac ischemia are broadly categories as angina and myocardial infarction, which are
explained as follows.
13
Chapter 1. Introduction
1.6.1.1
Angina
Angina is a serious chest pain caused by an imbalance between myocardial blood supply
and oxygen demand. The main cause of angina is atherosclerosis in the cardiac arteries.
1.6.1.2
Myocardial infarction
Myocardial infarction (MI) is commonly known as a heart attack. It occurs, when the
blood flow suddenly stops causing heart cells to die. This is most commonly due to
blockage of a coronary artery by plaque build up. The resulting ischemia and oxygen
shortage can damage or may lead to death (infarction) of heart muscle tissue.
1.6.2
Causes
Ischemic heart disease is caused by blockage of an artery due to plaque formation, usually
called atherosclerosis. Plaque formation narrows the artery, which makes blood to clot
easily and completely block the arteries. There are various causes of ischemic heart
disease, which are mentioned below.
• Ventricular tachycardia.
• Compression of blood vessels.
• Atherosclerosis.
• Extremely low blood pressure.
• Congenital heart defects.
• Sickle cell anemia.
1.6.3
Major risk factors
There are several major risk factors, which are mentioned below.
• Overweight and obesity.
• Smoking.
• Diabetes.
• Hypertension.
14
Chapter 1. Introduction
• Stress.
• High blood cholesterol.
• Drug abuse.
• Lack of physical activity.
• Coronary artery disease.
1.6.4
Different types of ischemia
Ischemia is classified into different types depending on the affected areas of the body
parts. Some major types are (i) Cardiac ischemia, (ii) Cerebral ischemia, (iii) Intestinal
ischemia, (iv) Critical limb ischemia.
1.6.4.1
Cardiac ischemia
In cardiac ischemia or myocardial ischemia flow of blood to the heart muscle is limited
by the blockage of a coronary artery. A sudden and severe blockage due to plaque may
lead to heart attack. Cardiac ischemia may also cause angina and arrhythmia. Following
are the typical symptoms of a myocardial ischemia.
• Difficulty in breathing.
• Pain in arm.
• Pain in chest.
• Pain in neck.
• Pain in jaw.
1.6.4.2
Cerebral ischemia
This type of ischemia takes place in the arteries of the brain due to restriction of blood
flow. A plaque is formed by blood clot in cerebral arteries. This plaque narrows down
the artery and blocks blood flow to brain. Some of the symptoms involved with cerebral
ischemia are mentioned below.
• Weakness.
15
Chapter 1. Introduction
• Unconsciousness.
• Difficulty speaking.
• Vision disability.
• Blindness.
• Body movement problems.
1.6.4.3
Intestinal or Bowel ischemia
Ischemic bowel disease occurs due to narrowing of the arteries, which leads to low supply
of oxygen needed to the intestines. The reduced blood flow may cause pain. This type
of ischemia may damage the intestine. Sign and symptoms of bowel ischemia [17] are
mentioned below.
• Sudden abdomen pain.
• Blood in stool.
• Black stool.
• Diarrhea.
• Constipation.
1.6.4.4
Critical limb ischemia
Critical limb ischemia occurs due to serious decrease in blood flow to hands, feet and
legs. Critical limb ischemia is often involved with severe peripheral arterial disease.
Following are the major symptoms of this type of ischemia.
• Severe pain in feet or toes even person is not moving.
• Thickening of the toenails.
• Skin infections or ulcers.
• Dry, black skin of the legs.
1.7
ST-segment analysis of ECG
Cardiac ischemia causes fluctuations in T-wave and ST-level of ECG. The ST-level
change episodes are useful for disease detection and diagnosis. Most of the clinically
16
Chapter 1. Introduction
useful information are found in the intervals and amplitudes of ECG waves. The characteristic features of ECG are the peak detection and time durations calculation. An
ECG graph with different peaks and intervals is shown in Figure 1.6. In a normal ECG,
the S-point is the first inflection point after R-peak. S-point is identified by determining
the change in slope. ST-slope is the important characteristic of ECG signal to investigate myocardial ischaemia. Elevation or depression of ST-segment provides important
features for detection of myocardial ischemia. Generally, ST-level is used to identify
ischaemic episodes. Elevation and depression of ST-segment together with changes in
T-wave amplitude can indicate the ischaemic disorder. ST-level deviation is measured
from the isoelectric level. The normal range of different waves and segments associated
with ECG are described in the Table 1.1.
Table 1.1: Description of different waves and segments in a ECG cycle
ECG Features
P-wave
Amplitude Duration
(ms)
(mV)
0.1 - 0.25 60 - 80
PRsegment
-
50 - 120
PRInterval
-
120 - 200
QRScomplex
STsegment
1
80 - 120
-
80 - 120
T-wave
0. 1 - 0.5
120 - 160
STInterval
QTinterval
RRInterval
-
320
-
300 - 430
-
600 - 1200
Description
The P-wave is the first wave of ECG and represents the sequential activation of the right and
left atria.
The PR-segment is the flat, usually isoelectric
segment between the end of the P-wave and the
start of the QRS-complex.
The PR-interval is the time duration from the
beginning of P-wave to the beginning of QRScomplex.
The QRS represents the simultaneous activation
of the right and left ventricles.
The ST-segment follows the QRS-complex. The
point at which it begins is called the J-(junction)
point.
The T-wave represents the period of recovery for
the ventricles .
The ST-interval is measured from the J-point to
the end of the T-wave.
The QT-interval is measured from the beginning
of the QRS-complex to the end of the T-wave.
The time elapsing between two consecutive Rwaves in the electrocardiogram.
17
Chapter 1. Introduction
PR- Interval
R
ST- Segment
P
T
J
QS
ST- Interval
PR- Segment
QRS interval
Figure 1.6: ECG graph with different peaks and intervals
1.7.1
ST-segment elevation
ST-elevation is a measurement on an ECG, in which the trace in the ST-segment is
very high above the isoelectric line. It identifies silent ischemia known as heart attack.
During heart attack, the coronary artery is blocked by the blood clot. This makes the
heart muscle to die. The changes in ECG characteristics identifies severe heart attack.
ST-segment elevation is one of the changes in ECG, where large amount of heart muscle
damage occurs. When ST-deviation is more than 0.08mV above the isoelectric line, it is
considered as positive ST-deviation or ST-elevation. The Figure 1.7 shows an elevated
ST-segment.
1.7.2
ST-segment depression
ST-segment depression is reciprocal of ST-elevation. The depression of ST-segment is
caused by rapid heart rate, electrolyte abnormality and ischemia. Change in the ST18
Chapter 1. Introduction
Elevated Baseline
Baseline of the ECG
Figure 1.7: ST-segment elevation
segment shape allows diagnosis of ST-depression. ST-deviation of more than 0.08mV
below the isoelectric line is considered as negative ST-deviation or ST-depression. STsegment depression is shown in Figure 1.8.
Baseline of the ECG
Baseline Depressed
Figure 1.8: ST-segment depression
1.7.3
T-wave
The normal duration of T-wave in ECG is 120-160ms [1]. The amplitude changes in
T-wave is also a distinguished factor for ischemic episodes detection. The abnormal
T-wave is usually very tall. It also appears as inverted corresponding to the elevation
or depression in ST-segment. The T-wave inversion or flattening is generally measured
using first 30s of the ECG recording. Different types of T-wave amplitude variations are
shown in the Figure 1.9.
1.7.4
Isoelectric line
The flat horizontal segments is the base or isoelectric line in an ECG cycle. It is the
portion of ECG between the end of T-wave and start of P-wave or between the end of
19
Chapter 1. Introduction
Tall
T-wave
Normal
Inverted
T-wave
Biphasic
T-wave
Flat
T-wave
Figure 1.9: T-wave alternation
P-wave and QRS-complex. The baseline is equivalent to 0mV line in a normal healthy
heart. The baseline may be depressed or elevated relative to the ST-segment in case of
heart disorders. The variation of ST-segment remains close to the isoelectric line. The
level of ST-segment is determined with respect to isoelectric level. If ST-segment is below
the baseline, then it is called ST-depression. During ST-depression, myocardium is not
getting enough oxygen which leads to myocardial ischemia. If the level of ST-segment is
above the baseline, then it is known as ST-elevation which leads to myocardial infarction.
The Figure 1.10 shows the position of isoelectric line in ECG signal.
1.8
European ST-T ECG database
The European ST-T database is used for evaluation of the proposed techniques. This
database consists of 90 annotated selections of ambulatory ECG recordings from 79
subjects. Myocardial ischemia was suspected for each subject and additional selection
criteria were established to identify ECG abnormalities in the database. The baseline
ST-segment displacement criteria was established from the conditions like hypertension,
20
Chapter 1. Introduction
ventricular dyskinesia and effects of medication. The database includes 367 episodes of
ST-segment change and 401 episodes of T-wave change, with durations ranging from
30 seconds to several minutes and peak displacements ranging from 100 microvolts to
more than one millivolt. Also this database includes 11 episodes of axis shift which
results apparent ST-change and 10 episodes of axis shift which results apparent T-wave
change. Each record is of two hours in duration and consists two signals, each sampled
at 250 samples per second with 12-bit resolution over a nominal 20 millivolt input range.
Two cardiologists worked independently to annotate each record beat-by-beat and for
changes in ST-segment and T-wave morphology, rhythm and signal quality. ST-segment
and T-wave changes were identified in both leads and their onsets, extrema and ends
were annotated [6].
1.9
Mobile health care
A term used for the practice of medicine and public health supported by mobile devices
like mobile phones, tablet computers, personal digital assistants (PDAs) etc., is known
as mobile health or m-health [18]. The field of m-health broadly encompasses, the use
of mobile telecommunication and multimedia technologies in health care delivery [19].
The term m-health was introduced by Robert Istepanian as use of “emerging mobile
R
R
Isoelectric line
T
P
Q
P
T
Q
S
S
Figure 1.10: ECG graph showing isoelectric line
21
Chapter 1. Introduction
communications and network technologies for health care" [20]. The development of the
m-health arises from the constraints which includes the following.
• high population growth, high burden of diseases, low health care workforce, large
numbers of rural patients and limited financial resources to support health information systems [21].
• rapid rise in mobile phone technology and availability of advanced health care
infrastructure mobile phone can deliver health care to the rural people at low
cost [20].
The objectives of m-health care are as follows.
• Increased access with low cost effective health care.
• Improved ability to diagnose and detect diseases.
• More public health information with for immediate action.
• Expanded access to ongoing medical education and training for health workers.
The short message service (SMS) or multimedia messaging service (MMS) and realtime voice communication technology are the backbone of mobile based health care system [19]. Increase in wireless infrastructure and mobile phone technologies can provide
health care to rural people in a faster way. The m-health field promotes a better health
care by communicating with the health care professionals located at a longer distance.
The advance mobile phone based technology also provides direct voice communication
and information transfer capabilities. Hence, advance in technology improves the information access capacity and two-way communication. However, in real time application
of m-health care faces many challenges during remote data collection and monitoring of
diseases. Remote monitoring and diagnosis of cardiac patient is little difficult in mobile
health care. Transmission and storage of large amount of ECG data of a cardiac patent
is a big problem in mobile phone based diagnosis of cardiac disorders.
22
Chapter 1. Introduction
1.10
GSM modem
A global system for mobile communications (GSM) modem is a wireless modem that
functions with available 2G networks. A wireless modem is a device which establishes
the communication between a PC and wireless network by generating, transmitting and
decoding data from a cellular network [22]. A wireless modem behaves like a dial-up
modem. The main difference is that a dial-up modem sends and receives data through
a fixed telephone line while a wireless modem sends and receives data through radio
waves. A GSM modem requires a subscriber identity module (SIM) card to operate,
but the modem is controlled by computers through AT-commands. Some standard AT
(ATtention)-commands are supported by both GSM modems and dial-up modems. GSM
modem also supports few extended sets of AT-commands. The extended AT-commands
are defined in the GSM standards which facilitates [23] the following.
• Sending of SMS.
• Writing, reading and deleting of SMS.
• Signal quality monitoring.
• Monitoring of battery charging status.
• Searching,reading and writing of phone book entries.
The writing and sending related AT-commands for SMS messages are given in Table 1.2.
The SMS can be received by connecting the modem to a computer. The computer uses
Table 1.2: AT commands used in writing and sending SMS
AT command
+CMGS
+CMSS
+CMGW
+CMGD
+CMGC
+CMMS
Meaning
Send message
Send message from storage
Write message to memory
Delete message
Send command
More messages to send
AT-commands to receive the SMS messages from the modem. The advantage of using
23
Chapter 1. Introduction
GSM modem for SMS is that the wireless network usually do not charges any fee for
receiving incoming SMS. AT-commands related to receive and to read SMS are given in
Table 1.3. To send and receive concatenated messages, it is generally recommended to use
Table 1.3: AT commands used in reading and receiving SMS
AT command
+CNMI
+CMGL
+CMGR
+CNMA
Meaning
New message indications
List messages
Read message
New message acknowledgement
GSM/GPRS (general packet radio service) modem with a computer. The concatenated
message contains message length more than 160 characters when 7-bit character encoding
is used [23].
1.11
Literature survey
The literature survey is done based on different ECG signal application, which are summarized in the following subsections.
1.11.1
On ECG signal compression
ECG is used to measure the electrical activity of the muscle fibers in different parts of
the heart. The variations in electrical potentials in 12 different directions are measured
and these 12 views of the electrical activity in the heart are normally referred as “leads".
A new approach for human identification was presented by Biel et al. [24]. An automatic
human identification technique was developed to identify a person in many different areas
of application. For an example, it can be used in security systems, where authorization
check is required. These tests are done with a standard 12-lead rest ECG where selected
features are extracted from the ECG to identify a person.
A number of techniques have been reported for efficient transmission of ECG signal
over wireless medium [25]. Generally ECG data from a patient usually takes large storage
space [5]. It is very difficult to store and transmit this over wireless medium for the
24
Chapter 1. Introduction
purpose of remote patient monitoring. Hence, it becomes essential to compress the data
for faster data transmission over bandlimited wireless medium. Compression processes
broadly categorized into two basic types like lossless and lossy. There is absolutely no
loss of information in lossless compression whereas the compression ratio is low. In
case of lossy compression the compression ratio is high while there is a marginal loss
of information. In most cases, lossy compression techniques are used for better data
compression performance [25]. Lossy compression technique are further classified into
three categories i.e., direct data compression, transform domain data compression and
parameter based data compression.
A direct data compression algorithm, amplitude zone time epoch coding (AZTEC)
was introduced by Cox et al. [26] to reduce redundancy in data sequence. This algorithm achieves compression ratio of 10:1. However, the reconstructed signal contains
discontinuities and distortion. A direct ECG data compression algorithm, turning point
(TP) [27] reduces the sampling frequency of the ECG signal and produces a compression ratio of 2:1. However, the reconstructed signal corresponding to the original signal
contains some distortion. A hybrid of AZTEC and TP algorithms, the coordinate reduction time encoding system (CORTES) was developed to reduce the distortion with a
compression ratio of 4.8:1 [28]. However, these algorithms cannot be applied to real-time
ECG data compression due to the complexity of computation.
A transform domain ECG data compression technique using discrete cosine transform (DCT) was inroduced by Aydin et al. [29]. This technique is further improved by
using dynamic threshold allocation and variable sub-band coding for enhanced performance [30]. A wavelet packet based algorithm was presented by B. Bradie [31] for the
compression of single lead ECG. This algorithm is compared with the karhunen-loeve
transform (KLT) technique. The wavelet packet algorithm generated significantly lower
data rates with better compression ratio. A two dimensional discrete cosine transform
method was presented by Lee and Buckley [32] for compression of ECG data. The
2-D DCT method shows redundancy between adjacent heartbeats and between adja25
Chapter 1. Introduction
cent samples. Ahmed et al. [33] reported that the best performance can be obtained if
the signal was decomposed up to the fourth level using non-orthogonal wavelet transform. The technique reported shows higher compression ratio and higher sampling rate.
A set partitioning in hierarchical trees (SPIHT) technique was developed by Huang
and Miaou [34]. SPIHT is a transform domain data compression technique for mobile
tele-cardiology, which uses 3G cellular phone standards. A wavelet transform based
international standard, joint photographic expert group (JPEG) was introdued by Bilgin et al. [35] for compression of still images. The method uses existing hardware and
JPEG2000 software coder and decoder for ECG compression. JPEG2000 codec retains
precise rate control and progressive quality of compression.
A parameter extraction technique was presented by Iwata et al. [36] for data compression. This algorithm is based on artificial neural network (ANN). A dual three-layered
(one hidden layer) neural network system is used for this purpose. The network is tuned
up with supervised signals as input signals. The back propagation is used as the learning
algorithm. Data compression is accomplished by storing the activation levels instead of
the original signal. Szilagyi et al. [37], presented a parameter extraction technique for
ECG compression. It uses an adaptive entropy coder to obtain 10 times less redundancy
than an optimized Huffman coder. Kyoso and Uchiyama presented a microprocessor
based transmitter in [38] that reduced ECG data by base line drift canceller, waveform
detector and wave analyzer for transmitting the diagnosis information only. An ECG
compression algorithm presented by Diaz-Gonzalez et al. [39], which uses a max-lloyd
quantizer to optimize the low resources of an ECG acquisition and transmission system. This algorithm scheme is based on a first-order differential pulse code modulation
(DPCM). The non-uniform quantizer results low distortion in the reconstructed signals
due to its low computational complexity. The compression process could be accomplished on-line during the ECG acquisition process. An error effect was reported by
Alesanco et al. [40] for real-time ECG monitoring in a wireless tele-cardiology application. This technique is based on wavelet compression codec. Both quantitative error and
26
Chapter 1. Introduction
qualitative opinions were presented in order to monitor retrieved information from ECG
packets. Nait-Ali et al. presented a method for ECG compression in which is based
on three major approaches, Time Division Multiplexing (TDM) and multilevel wavelet
decomposition followed by parametrical modeling. Pre-processing has been carried out
before applying these techniques for detecting and aligning different beats. Lee et al.
introduces a real-time data compression and transmission algorithm between e-health
terminals for a periodic ECG signal in [25]. Transform domain lossy type compression
method has been applied to achieve a high compression ratio.
1.11.2
On ECG data transmission over wireless medium
A modelling concept of global system for mobile communications (GSM) based mobile
tele-medicine system was presented by R. S. H. Istepanian [41]. This system shows
successful multichannel mobile transmission of medical data with low bit error rates. A
prototype integrated mobile telemedicine system was introduced by B. Woodward et al.
[42], which is compatible with existing mobile telecommunications networks. This system
will enable a doctor to monitor a patient remotely. A SMS based design presented by R.
G. Lee and K. C. Chang [43] consists of a transmitter and a controller for a portable, light
weight and small size tele-alarm device. In an emergent situation, when a heart stroke
occurs, the user only needs to push a button to trigger the controller. The controller
automatically sends stored text messages from its database through the transmitter to
the specified mobile phone numbers. A new system was presented by S. Borromeo et
al. [44] for ECG acquisition and wireless transmission purpose. A modular hardware
system design based on a field-programmable gate array (FPGA) is also presented for
development and debugging purpose. F. Sufi et al. [5] presented an ECG compression
algorithm, which allows transmission of compressed ECG over bandwidth constrained
wireless link through multimedia messaging service (MMS), SMS and hypertext transfer
protocol (HTTP). A wide-area wireless ECG transmission technique was presented by
A. Alesanco and J. Garcia [45] for real-time cardiac tele-monitoring. The technique
27
Chapter 1. Introduction
uses a new protocol for retransmissions of erroneous packets, which will reduce possible
negative effects. M. Kamel et al. [46] presented a design of a low cost secure system for
data acquisition and visualization in mobile devices. Its design allows easy technological
updates and developments. U. Goel et al. [47] introduced an application facility to send
SMS without the need of an internet service. This application uses a GSM or GPRS
modem and a subscriber identity module (SIM) card to send messages to any mobile
network. The cost of the message sent is based on the message tariff subscribed with
the SIM card. A mobile phone or GSM/GPRS modem is connected to a computer. The
instructions called AT(ATtention)-commands are used to control the mobile phone.
1.11.3
On detection of cardiac disorders
An automatic technique for analysis of cardiac abnormal rhythms or cardiac dysrhythmia was presented in [48]. However, this study suggests that automatic dysrhythmia
monitoring makes more robust management of dysrhythmia. This technique requires a
better processing algorithm for more analysis on the ECG signal. Ozbay et al. [49] presented a study on artificial neural networks (ANN) in to classify the ECG arrhythmias.
The different structures of ANN have been trained by arrhythmia separately and also
by mixing with 10 different arrhythmias. An idea to develop a bio-signal processing tool
that can predict possibility of future risk of abnormalities in ECG signals was presented
by H. H. Namarvar and A. Vahid. Shahidi [50]. A singular value decomposition analysis
of spectral energy distribution in time frequency plane is applied to extract features and
cardiac arrhythmias are classified using support vector machines. This method allows
an early detection and reduces the risk of cardiac arrhythmias. A real-time continuous
arrhythmias detection system (RECAD) was presented by Zhou et al. [51], which is
based on the wireless sensor network technology. The ambulatory wireless ECG sensor
captures and analyzes the patient’s ECG signal in real-time. When a cardiac abnormal
event is detected, an alarm message is sent to the local access server via local wireless
technologies, such as WiFi, bluetooth or digital radio communication. The cardiologist
28
Chapter 1. Introduction
evaluates the received message according to the physical state of the patient. The average cardiac arrhythmia detection rate is found to be 95%. A wireless tele-monitoring
system was presented by Ibaida et al. [52] to analyse the compressed ECG signal for diagnosis of ventricular tachycardia. This system uses principal component analysis (PCA)
for feature extraction and k-mean for clustering of normal and abnormal ECG signals.
However, decompression in wireless tele-monitoring causes delay on the doctor’s mobile
devices. A mobile heart rhythm tele-monitoring system was presented by Mateev et
al. [53], which evaluates the clinical applicability and patient compliance. This system
shows similar results to a standard holter ECG, but the disadvantage is that this system
did not provide complete diagnosis.
A computer-based system was presented by Hsia et al. [16] for diagnosis of abnormal
rhythm and ST-segment in an exercise system. Digitized data are analyzed in a beatby-beat mode. Each beat is assigned a beat code based on a combination of waveform
analysis and RR-interval measurement for abnormal rhythm analysis. Baseline wander
is a major problem in exercise ECG which makes accurate reading extremely difficult.
This system provides accurate ST-level and slope measurements but it requires more
computation time. K. Wang [54] presented a method for recognizing the shape of STsegments. This method is based on the approximation that ST-segment is either a line
segment or a parabolic segment. The estimation of ST-segment endpoint is very much
difficult which causes implementation of the method practically impossible. Maglaveras et al. [55] presented an automatic ischemic episodes detection algorithm, which is
based on a supervised neural network. The performance of ischemic episodes resulting from ST-segment elevation or depression are measured using the European ST-T
database. This neural network (NN) based algorithm implementation provides fast and
reliable detection of ischemic episodes whereas training of the neural network is time consuming. An algorithm based on nonlinear principal component analysis was presented
by Stamkopoulos et al. [56] for detection of ischemic episodes. The feature extraction
method is nonlinear and it is implemented using a multilayered neural network. Garcia
29
Chapter 1. Introduction
et al. [57] presented a new detector to determine changes in the repolarization phase
(ST-T complex) of the cardiac cycle. The advantage of this detector is that it finds both
ST-segment deviations and entire ST-T complex changes. A new myocardial ischemia
indicator presented by Lemire et al. [58], which examines the information content of a
combined ST-segment and T-wave complete morphology through fast wavelet transform
(FWT) and Shannon’s entropy. An automatic algorithm was presented by A. Smrdel
and F. Jager [59] for detection of time varying episodes of ST-segment. The algorithm
tracks the ST-segment reference level to detect the changes in ST-episodes. F. Jager et
al. [60] developed an automatic system to detect transient ST-segment in ECGs. The
work was challenging and realistic research resource for development. Exarchos et al. [61]
presented an automated methodology, which is based on association rules for the detection of ischemic beats in long duration electrocardiographic recordings. A limitation of
the methodology is that it requires a representative training set in order to extract reliable rules. An automatic technique for detection of ST-segment deviation was presented
by Afsarl et al. [62] to diagnose the coronary heart disease (CHD). The lead-dependent
karhunen-loeve transform (KLT) bases are applied to reduce the ST-segment data. Faganeli et al. [63] reported that ischemia is presented by transient ST-segment episodes.
It occurs due to increase in heart rate. An automated system for on-line monitoring and
detection of ST-changes was presented by Mohebbi et al. [64]. In this system a normal
beat template is used as reference. A set of rules based on ST-slope or ST-deviation
measurements are defined by cardiologists for detection of ischemic beats. A window
classification is used for detection of ischemic beat sequences. The performance of the
system results a high sensitivity and good positive predictivity. Its main advantages are
short processing time and acceptable accuracy.
1.12
Objectives of the Thesis
Aim of the thesis is to develop the algorithms for establishing an efficient and faster
transmission of ECG signal to a health care centre or hospital over wireless medium and
30
Chapter 1. Introduction
to develop an automatic cardiac abnormalities detection technique from compressed ECG
signal. At health care center, the received compressed ECG signal is utilized to detect
cardiac disorders like bradycardia, tachycardia and ischemia. The specific objectives of
the thesis work are as follows:
• Development of an automatic ECG signal compression technique based on empirical mode decomposition (EMD). EMD based approach is a data adaptive process
which allows an iterative decomposition of the signal into a series of functions
known as intrinsic mode functions (IMFs). It is convenient to analyze nonlinear
and non-stationary data at instantaneous frequency (IF).
• To develop an algorithm for transmission of compressed ECG data and to reconstruct the ECG signal from the received compressed data. A GSM modem with
SIM card communicates PC using AT-commands to transmit compressed ECG in
the form of SMS which is a cost effective off-line process. The received SMS messages are transferred to PC using blue-tooth and the data decompression processes
are carried out to reconstruct the ECG signal.
• Development of an algorithm for detection of i) abnormal heart rhythm like bradycardia and tachycardia through HR calculation from decompressed ECG signal and
ii) ischemic episodes using European ST-T database through the measurement of
ST-segment deviation as well as T-wave amplitude changes relative to isoelectric
line.
31
Chapter 1. Introduction
1.13
Thesis Organization
Thesis is organized as follows.
• Chapter 1 introduces anatomy of the human heart, importance of ECG signal
and standard ECG databases. Different types of cardiovascular disorders are also
presented in this chapter. The generation of ECG signal from electrical activity
of heart muscles is also presented. This chapter also introduces mobile health
care system and the facilities to use wireless modems. The literature studies on
ECG signal compression, wireless ECG data transmission and detection of cardiac
disorders are described in this chapter.
• Chapter 2 elaborates the necessity of ECG signal compression. Theoretical background of empirical mode decomposition (EMD) technique is also described in this
chapter. The proposed methodologies for ECG signal compression and decompression are also presented in this chapter.
• Chapter 3 explains about the transmission of the compressed ECG data over wireless medium. This chapter also describes the SMS based efficient transmission of
the compressed ECG. The techniques used to reconstruct the original from the
received text SMS messages are also presented in this chapter.
• Chapter 4 presents the proposed algorithm for detection of cardiac disorders. The
methodologies for detection of heart dysrhythmia abnormalities (like bradycardia
and tachycardia) and ischemic episodes are also described in this chapter.
• Chapter 5 concludes the whole work and also discusses the scope of future work.
32
CHAPTER 2
ECG SIGNAL COMPRESSION AND
DECOMPRESSION
2.1
Introduction
The electrocardiogram (ECG) is a graphical recording of the electrical signals generated
from the heart [1]. ECG is used as a diagnostic tool for cardiac patient monitoring and
diagnosis of heart disorders. ECG provides valuable diagnostic information about functioning of heart and cardiovascular system. A patient located at a rural cardiac diagnostic center faces lots of difficulties in getting appropriate treatment due to unavailability
of advance equipments and specialist doctors. In computer based technology, faster and
efficient diagnosis makes a big difference in saving a patient’s life. ECG data from a
cardiac patient in a real time monitoring process can grow up to 2.77 GB in one day [5].
Storage and transmission of such a large amount of data is very difficult in real time
applications using wireless communication technology. It is also very much difficult for
rural patients to get faster diagnosis from expert doctors available in developed cities.
Hence, it becomes essential to compress the ECG data for establishing faster diagnosis
through wireless medium.
Several algorithms [26–29,31,33,37,65] have been developed for compression of ECG
signal. The lossless compression [66–68] preserves all information, while the compression
33
Chapter 2. ECG Signal Compression and Decompression
ratio is low. In case of lossy compression the compression ratio is high in expense of
marginal loss of information. In the field of information technology lossy compression
methods are used to represent the information content. Lossy compression technique
reduces the amount of data needed to store or transmit by removing redundant and
unnecessary information. Lossy ECG compression methods have been presented in [69–
71]. It has been shown in previous work [72], that lossy compression of ECG signal
results in information loss within the acceptable limit. This marginal loss of information
hardly affects important morphological features of ECG signal. In this work, a lossy
compression scheme is adapted for remote patient monitoring.
In most cases, lossy compression techniques are used for better data compression
performance compared to lossless encoding techniques [25]. Moreover, for remote health
care systems, the ECG data need to be compressed sufficiently so that easy transmission
of data is possible over wireless medium. Several wireless transmission based on lossy
compression [42, 43, 45–47, 73] techniques have been developed for remote patient monitoring. The aim of the compression technique is to attain maximum data reduction and
to preserve the significant signal features.
2.2
Types of compression
ECG data needs to be compressed for efficient data storage and faster transmission over
wireless medium. Data compression is the process of eliminating redundant information
so as to attain maximum data volume reduction and preserve important information on
reconstruction. The size of data reduces considerably by compression which makes memory space available for easy storage of digitized ECG in a storage device. Particularly
in tele-health care (i.e., tele-cardiology) purpose, ECG data need to be transmitted efficiently so that the proper diagnosis can be made by cardiac specialists located away from
the remote health centers. Thus, main goal of ECG data compression is easy storage
and faster transmission over long distance. Compression processes broadly categorized
into two basic types i.e., lossless and lossy.
34
Chapter 2. ECG Signal Compression and Decompression
2.2.1
Lossless techniques
The original data can be exactly retrieved from their compressed form if data are compressed using lossless techniques. There is absolutely no loss of information in lossless
technique [74]. There are many situations like text compression, radiological image compression, etc., where, it is required that reconstructed data to be identical to the original.
Hence, lossless compression techniques are suitable for this purpose. An important area
for application of lossless technique is compression of text data. The lossless technique
such as Huffman coding is widely used for compressing textual data [75]. The lossless
compression [66–68] preserves all information, while the compression ratio is low. There
are situations where lossy compression is used to get more data compression.
2.2.2
Lossy techniques
The unnecessary or redundant information are eliminated by focusing more on saving
space over preserving the accuracy of the data. In lossy compression, only an approximation of the original data can be retrieved. In most cases, lossy compression techniques
are used for better data compression performance as compared to lossless encoding techniques [25]. Hence, the lossy compression techniques are suitable for remote patient
monitoring. The techniques used in ECG compression evaluates the compression efficiency in terms of compression ratio (CR) [28] and percent root mean square difference
(PRD) [27]. The CR is defined as ratio of original data input to the compressed output
data. The PRD is the error difference between original signal before compression and
reconstructed signal. As stated in [25], lossy compression technique for ECG data are
further classified into three categories.
• Direct data compression
• Transform domain data compression
• Parameter extraction based data compression
35
Chapter 2. ECG Signal Compression and Decompression
2.2.2.1
Direct data compression
The direct data compression techniques attempts to reduce redundancy in data sequence
by examining a successive number of neighboring samples both previous and future.
Commonly used direct data compression methods are amplitude zone time epoch coding
(AZTEC) [26, 76], turning point (TP) [27], coordinate reduction time encoding system
(CORTES) [28] and the Fan [77].
2.2.2.2
Transform domain data compression
In the transform domain techniques, redundancy is reduced by applying linear transformation to the signal and then compression is applied in the transform domain instead of
time domain. It transforms the original data into a domain that more accurately reflects
the information content. The reconstruction of the signal is done by inverse transformation with a certain percentage of error. The transform domain method converts the time
domain signal to frequency domain or other domains [25]. For an example, the transform
domain techniques includes Fourier transform, Fourier descriptor [78], Karhunen Loeve
transform (KLT) [65], the Walsh transform, discrete cosine transform (DCT) [79] and
wavelet transform [80].
2.2.2.3
Parameter extraction based data compression
In the parameter extraction technique, the extraction of a set of useful parameter from
the original signal is carried out and the same are used in the reconstruction process [81].
Some of the methods includes namely peak-picking, neural network method and parameter extraction method [37]. The peak picking compression technique presented in [82]
is based on the sampling of a continuous signal at maxima and minima. The extraction
of signal parameters carry the information about the signals. Nowadays, artificial neural networks (ANN) are used for pattern recognition and classification problems. The
important features of ANN based techniques exhibit adaptation or learning. For example, using ANN for ECG compression, Iwata et al. [36] presented a data compression
36
Chapter 2. ECG Signal Compression and Decompression
algorithm for holter recording with ANN. A dual three-layered neural network system is
used for this purpose. The back propagation algorithm is used as the learning technique.
In general, lossy compression technique uses transform coding to get highly compressed data. It transforms the original data into a domain that more accurately reflects
the information content. The most suitable transform domain compression technique is
wavelet transform, which allows the analysis of non-stationary signal. However wavelet
based analysis faces difficulties like uniformly poor resolution due to limited size of the
basic wavelet function and its nonadaptive nature. The limited length of the basic
wavelet function makes the quantitative definition of the energy-frequency-time distribution difficult [83]. Sometimes, the interpretation of the wavelet can also be counter
intuitive for ECG signal analysis. Wavelet transform is having better frequency resolution and poor time resolution for low frequencies and vice versa for high frequencies [84].
For example, if a change of the ECG signal is occurred locally, it is required to look
the result in the high-frequency range of the signal. To define the local events in low
frequency range of the ECG signal still it is required to look for its effect in high frequency range of the signal [83]. Such interpretation will be difficult to analyze an ECG
signal from a wavelet based method. Another difficulty of the wavelet analysis is its
non-adaptive nature i.e. once the basic wavelet is selected, one will have to use it to
analyze all the data. A data adaptive technique is applied in this thesis to overcome the
difficulties [83]. This technique is based on empirical mode decomposition (EMD) [85],
which analyses the non-stationary signal in detail.
2.3
Empirical Mode Decomposition
A data adaptive method to analyse non-stationary signal is based on empirical mode
decomposition (EMD) [85]. In EMD the bases are derived from the multivariate data
which are nonlinear and non-stationary. Time-frequency analysis of nonlinear and nonstationary data requires a multi scale approach at the accuracy level of instantaneous
frequency (IF). Hilbert transform (HT) is convenient to analyze nonlinear and non37
Chapter 2. ECG Signal Compression and Decompression
stationary data at IF. Some standard transform methods are not suitable for analysis of
nonlinear and non-stationary data due to certain limitations like:
• Fourier and wavelet approach employs predefined basis functions (harmonic, mother
wavelet) which are fixed bases.
• In time-frequency analysis, the accuracy depends on data length and stationary
patterns, which are short and irregular. The integral transforms representation
makes trade-off in frequency resolution.
• Standard patterns in data occur at their own intrinsic scales and thus provides
inadequate measurement.
The advantages of data driven approach, EMD is that the components are derived
empirically from the data and holds the properties like,
• Data-adaptive, which facilitates the intrinsic patterns at multiple scales, while not
requiring the rigid assumptions of harmonic or stationary data.
• Enhanced accuracy, which predicts the time frequency accuracy at the IF level and
a natural account of nonlinearity.
• The integrity of multivariate bases which facilitates synchronization, causality and
data association.
The EMD based aproach allows an iterative decomposition of the signal into a series of
functions known as intrinsic mode functions (IMFs). Theoretically, each intrinsic mode
function (IMF) which is a simple oscillatory component extracted from original signal,
contains all frequency from highest to lowest. IMFs are obtained from the signal by
means of sifting process [83]. As reported in Huang et al. [83], an IMF is a function
which must satisfy two conditions: (a) the number of extrema must either be equal to
or at most differ by one from the number of zero crossings. (b) the mean values of both
the envelope defined by the local maxima and the envelope defined by the local minima
are zero at any point in the data. For an example, assume that a temporal continuous
38
Chapter 2. ECG Signal Compression and Decompression
time signal is x(t). The sifting process [83] applied to x(t) consists of various processes
which are represented as per [86].
1. Find the location of all the extrema (both maxima and minima) of x(t).
2. Interpolate (cubic spline fitting) between all the maxima extrema ending up with
entire upper envelope xmax (t).
3. Interpolate (cubic spline fitting) between all the minima extrema ending up with
entire lower envelope xmin (t).
4. Compute the mean envelope between upper envelope and lower envelope m(t) =
xmax (t)+xmin (t)
.
2
5. The IMFs are calculated using number of iterations. The difference between the
data x(t) and the mean m(t) is the first component of IMF which is given as
h1 (t) = x(t) − m(t). The component h1 (t) is an IMF, if it satisfies the conditions
of IMF otherwise it is calculated iteratively with stopping criteria. If IMF criteria
is not satisfied, h1 (t) is treated as the data input to the second sifting (iterative)
process. The second component is the difference between the data h1 (t) and the
mean m11 (t) that is h11 (t) = h1 (t)−m11 (t). The sifting process is repeated ‘k’ times
until h1k (t) is an IMF, that is h1k (t) = h1(k−1) (t) − m1k (t). A stopping criterion is
employed to the number of sifting iterations which is obtained by limiting the size
of the standard deviation (SD), computed from the two consecutive sifting results.
SD =
[
P
|h1(k−1) (t)−h1k (t)|2
].
h21(k−1) (t)
Here SD is predefined to be very small and iterative
calculations are carried out so long as the stopping criteria is not met. The residue
is calculated as r1 (t) = x(t) − h1k (t).
6. After the IMF is found the residue is calculated as rv (t) = r(v−1) (t) − h(v−1) (t),
for v is 2, 3, 4, ....., M.
rv (t) is the data that should be treated as input at the calculation of v th IMF.
Clearly if v = 1 for IMF 1, then r1 (t) = r(0) (t) − h(0) (t), where, r0 (t) = x(t) and
h0 (t) = m1 (t)
39
Chapter 2. ECG Signal Compression and Decompression
7. For calculation of ‘M’ number of IMFs, step 1 to 6 is repeated.
If ‘M’ rounds of sifting process is performed on the given signal x(t), it will be decomposed
to a set of ‘M’ IMFs and a residue signal which can be denoted as
x(t) =
M
X
hk (t) + rv (t)
k=1
The above equation shows that a signal which is decomposed by EMD can be reconstructed easily by simple addition of the IMF components hk (t) and the residue signal
rv (t).
2.4
2.4.1
Proposed Framework
Compression
The methodology followed for ECG data compression consists of following stages: signal
decomposition through empirical mode decomposition (EMD), downsampling, discrete
cosine transform (DCT), window filtering and Huffman encoding. The work flow diagram
for the signal compression process is shown in Figure 2.1. All the stages are explained
as follows.
2.4.1.1
EMD based signal decomposition
The ECG signal is decomposed into a series of IMFs using EMD technique. The last
IMF is called the residue. As an example Figure 2.2 represents the EMD decomposition
of the ECG signal taken from European ST-T ECG record tape no. # e0613. It contains
sixteen IMFs and one residue. Out of 16 IMFs, first two IMFs (i.e., IMF 1 and IMF
2) are removed as these are high frequency noise components. Therefore, by removing
first two IMFs, mostly noise components of the signal are removed. The remaining IMFs
including the residue signal are further processed in the next stage.
2.4.1.2
Downsampling
The common idea of downsampling is to reduce the cost of processing. Generally, memory required to implement a signal processing system is proportional to the sampling
40
Chapter 2. ECG Signal Compression and Decompression
ECG
Signal Data
EMD based
Signal Decomposition
Removal of noisy components
IMF 1 and IMF 2
Considering remaining IMFs
IMF 3
IMF 4
Downsampling
Downsampling
.
.
.
.
. . . .
Residue
Downsampling
Discrete Cosine
Transform (DCT)
Window Filtering
Huffman Encoding
Compressed
ECG Data
Figure 2.1: Work flow diagram for ECG signal compression
rate. The down sampled data can be used as an input to a device which is operating
at a low sampling rate. The downsampling factor (D) is obtained using the minimum
distance between extrema points in the first IMF (i.e., IMF 1) [86]. The equation to
calculate down sampling factor is given as D =
Emin
2 ,
where Emin , is the minimum
distance between two consecutive extrema points in IMF 1. The downsampled factor is
found to be ‘2’. Thus half downsampling is applied to the all IMFs excluding the first
two noisy IMFs (i.e., IMF 1 and IMF 2). The downsampled signals are summed up and
41
Chapter 2. ECG Signal Compression and Decompression
Amplitude
(mV)
−400
−600
Input
ECG
Signal
−800
−1000
0
100
200
300
400
500
600
No. of samples
Amplitude
(mV)
Amplitude
(mV)
IMF1
0
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0
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(mV)
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(mV)
−100
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0
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mV)
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(mV)
IMF3
0
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IMF10
0
−50
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IMF4
0
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(mV)
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(mV)
200
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1000
0
−50
1000
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IMF12
0
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20
IMF5
0
−50
IMF11
0
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(mV)
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(mV)
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0
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IMF13
0
−20
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50
0
IMF6
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(mV)
50
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(mV)
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(mV)
IMF7
0
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IMF14
0
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(mV)
0
50
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−50
0
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IMF15
0
1000
0
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20
IMF8
0
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(mV)
50
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(mV)
IMF9
0
50
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1000
50
0
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900
0
−100
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800
100
100
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700
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0
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IMF16
0
1000
0
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600
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800
1000
Amplitude
(mV)
−900
Residue
−905
−910
0
200
400
600
No of samples
800
1000
Figure 2.2: Decomposition of input ECG signal (European ST-T record no. # e0613)
fed as input to the next stage.
2.4.1.3
Discrete Cosine Transform (DCT)
DCT is a transform based ECG compression methods compression method which uses
orthogonal transform to the signal [87]. It is used to reduce the redundancy present
42
Chapter 2. ECG Signal Compression and Decompression
in the signal. DCT is generally used for data compression due to its greater ability
to concentrate the signal energy in few transform coefficients. Only a few coefficients
contain information about the real signal while others appear as less important details
[87]. The real DCT coefficient makes it simpler for efficient implementation. DCT
expresses the signal in terms of sum of cosine functions with different frequencies and
amplitudes. The frequency domain signal is represented as forward DCT, C(k). The
DCT of a signal x(n) of length N is defined by
C(k) = α(k)
N
−1
X
x(n)cos
n=0
1
2π
(n + )k,
N
2
(2.1)
k = 0, 1, 2, ...., (N − 1),
n = 0, 1, 2, ...., (N − 1).
where, C(k) is the kth DCT coefficient and the scale factor α(k) is defined as
α(k) =



q
1
qN
2
N
f or k = 0
f or k 6= 0
DCT operates on a function at a finite number of discrete data points [88] and DCT is
applied because of following characteristics: (i) it maintains the periodicity of an ECG
signal. (ii) it enables high compression rate during Huffman coding [25]. (iii) it provides
high de-correlation and energy compaction property [89].
2.4.1.4
Window Filtering
In this application, windowing is used for decomposing the long duration signals into
shorter duration. The characterstics of the signal remains stationary over short duration
of window [90]. Furthermore, the occurrence of false discontinuities at the edges of
the signals are eliminated. A rectangular window function is applied to the discrete
cosine transformed signal which modifies the discontinuities at the edges [91]. For an
example, the size of the window is chosen to accommodate 1000 samples. The signal is
sampled at 250Hz, it means 4 sec data is available for a particular window. The signal
43
Chapter 2. ECG Signal Compression and Decompression
after compression can be transmited for cardiac disorder detection. As the RR-interval
duration varies from 600-1200ms, it can be ensured that the signal of 1000 samples can
cover all the information for detection of cardiac dysrhythmia.
2.4.1.5
Huffman encoding
The final step for the ECG compression algorithm is Huffman or variable length coding.
Huffman coding is based on frequency of occurance of data points in a data stream [75].
The principle is to allocate minimum number of bits to a datum that appears more
frequently. Huffman code can be briefly summarized as follows.
1. Initialization: Put all distinct data points of a data stream in a list and sort them in
ascending order. Each distinct datum is called a node.
2. Repeat the following steps until list has only one node left.
a. From the list pick two nodes having lowest frequency and create one parent node
of them.
b. Assign the sum of children’s frequency to the parent node and insert it into the
list. This create a tree like structure.
c. Assign 0, 1 to the two branches of tree and delete the children from the list.
Huffman code can reduce the redundant information with a group of codes. It provides an
optimal coding length in terms of average value of bits per sample as well as it reduces
cost of encoding. The method is that a binary code is assigned to the data whose
length is variable. The basic idea is to assign fewer bits (i.e. codewords) to frequently
occurring data (those having higher probabilities) and more bits to less occurring data
(those having lower probabilities) [75]. The compressed ECG signal is encoded as text
for transmission over wireless networks.
2.4.2
Decompression
The signal can be reconstructed from the compressed data by applying decompression
procedure. The reconstruction process is in the reverse order of compression methods,
44
Chapter 2. ECG Signal Compression and Decompression
which are explained below. The work flow diagram for reconstruction of ECG signal is
shown in Figure 2.3.
Compressed
ECG Data
Huffman Decoding
Inverse DCT
Spline
Interpolation
Reconstructed
ECG Data
Figure 2.3: Work flow diagram for ECG signal reconstruction
2.4.2.1
Huffman decoding
The Huffman decoding or inverse Huffman coding is the reverse process of Huffman
encoding. In the process of Huffman encoding a Huffman tree is generated with a root
and its leaves. The Huffman tree is helpful while decoding the encoded data. In decoding,
Huffman-encoded file has to be read starting with the first bit in the stream and then
uses successive bits from the stream to determine whether to go left or right [75]. When
a leaf is reached, a character is decoded and placed on the output of the stream. The
next bit in the input stream is the first bit of next character. Again same procedure is
followed until each character in the stream is decoded. The amount of decoded data may
contain only the important information which will be carried by the DCT coefficients.
45
Chapter 2. ECG Signal Compression and Decompression
2.4.2.2
Inverse Discrete Cosine Transform (IDCT)
Signal can be reconstructed accurately from only few DCT coefficients those carries important information about the signal. The time domain signal x(n) can be reconstructed
from forward DCT signal, C(k) by using IDCT. The time domain signal reconstructed
contains less number of samples for the specified window length. The number of samples
can be increased for accurate reconstruction of the required signal by using more number
of data points. The discrete time signal x(n) can be given by IDCT.
x(n) =
N
−1
X
α(k) C(k)cos
k=0
1
2π
(n + )k,
N
2
(2.2)
n = 0, 1, 2, ...., (N − 1),
k = 0, 1, 2, ...., (N − 1).
where, C(k) and α(k) are the parameters as defined in previous section.
2.4.2.3
Spline Interpolation
The data received are upsampled using spline interpolation method to get uniform sampled data. This process uses the cubic spline interpolation to reconstruct the original
signal [25]. The cubic spline method provides less distortion which in turn reconstruct a
smooth signal. The cubic splines are most desirable interpolation scheme because other
lower order interpolation scheme like linear or quadratic can cause errors in estimation
of the maxima and minima [92]. Using this interpolation scheme the required ECG signal is reconstructed effectively. Spline interpolation is to draw smooth curves through a
number of points. The spline consist of weights attached to a flat surface at the points
to be connected. The points are numerical data. The weights are the co-efficient on
the cubic polynomials used to interpolate the data. These co-efficients bend the line so
that it passes through each of the data points without any erratic behavior or breaks in
46
Chapter 2. ECG Signal Compression and Decompression
continuity [93]. The idea is to fit a piecewise function of the form,
S(x) =


s (x)

 1


s2 (x)
if x1 < x < x2
if x2 < x < x3
..


.




sn−1 (x) if xn−1 < x < xn
where si is a third degree polynomial defined by
si (x) = ai (x − xi )3 + bi (x − xi )2 + ci (x − xi ) + di ,
for i = 1, 2, 3, . . . , n − 1.
S(x), S ′ (x) and S ′′ (x) should precisely continuous over x1 to xn . The coefficients are
given by
ai =
Mi+1 −Mi
6h
bi =
Mi
2
ci =
yi+1 −yi
h
− ( Mi+16+2Mi )h
di = yi where yi = S(xi ), Mi = s′i (xi ) and h = xi − xi−1 .
The resulted spline interpolated signal is the reconstructed ECG. The stepwise output
signals of the decompression process are shown in Figure 2.4.
2.5
Experimental Results and Discussions
The proposed EMD based compression algorithm is evaluated using European ST-T
database [6]. The compression ratio (CR) and percent RMS difference (PRD) for different ECG signals of European ST-T data base using the proposed method are given in
Table 2.1. The CR is defined as,
CR =
Ninp
Nout
(2.3)
where, Ninp is the size of original signal and Nout is the size of compressed signal. The
average CR and PRD for selected ECG signals is found to be 23.5:1 and 1.38 respectively.
For comparison purpose, the proposed EMD based compression algorithm is evaluated
by using MIT-BIH arrhythmia database [7].
47
Chapter 2. ECG Signal Compression and Decompression
Amplitude
(mV)
500
(a)
0
−500
0
100
200
300
No. of samples
400
500
Amplitude
(mV)
−600
(b)
−800
−1000
0
100
200
300
No. of samples
400
500
Amplitude
(mV)
−600
(c)
−800
−1000
0
200
400
600
No. of samples
800
1000
Figure 2.4: Signals at different stages of ECG signal reconstruction
(a) Huffman decoded Signal (b) Signal after IDCT (c) Reconstructed ECG
The MIT-BIH arrhythmia database is used to conduct research on arrhythmia analysis. This database is put together by Massachusetts Institute of Technology (MIT) and
Beth Israel Hospital (BIH). In 1980, this database was the first generally available set of
standard test material for evaluation of arrhythmia detectors and also for basic research
into cardiac dynamics. The MIT-BIH arrhythmia database contains 48 half-hour selecTable 2.1: Evaluation of CR and PRD for European ST-T database
ECG Record
e0105
e0112
e0122
e0127
e0207
e0211
e0404
e0417
e0606
e0613
e0615
e0704
CR
23.26
23.28
23.81
23.26
23.28
23.26
23.83
23.82
23.27
23.29
23.81
23.82
48
PRD
0.71
6.92
0.384
0.56
4.22
2.31
0.1
0.243
0.13
0.22
0.46
0.24
Chapter 2. ECG Signal Compression and Decompression
tions of two-channel ambulatory ECG recordings, obtained from 47 subjects studied by
the BIH arrhythmia laboratory between 1975 and 1979 [7]. The recordings were digitized
at 360 samples per second per channel with 11-bit resolution over a 10 mV range. Each
beat in these records are properly annotated by a set of expert cardiologists.
The performance of the proposed method is evaluated using CR and percent RMS
difference (PRD) values. The CR values are obtained as per the equation 2.3. The PRD
is represented to evaluate the reconstruction efficiency. The signal after decompression
is compared with the original signal and the PRD is expressed as,
v
u PN −1
u
(xs (n) − xr (n))2
× 100
P RD(%) = t n=0PN −1
2
n=0
xs (n)
(2.4)
where, xs (n) is the original signal before compression, xr (n) is the reconstructed signal
and N is the total number of data instances. The experimental values of CR and PRD
using MIT-BIH arrhythmia database [7] are given in Table 2.2. The average CR and PRD
are found to be 23.74:1 and 1.49, respectively. The evaluation parameters CR and PRD
are also compared with the result presented by other researchers for the ECG record no.
# 117. In comparison to earlier direct time-domain lossy compression techniques [26–28,
77], the proposed transform domain lossy compression technique shows high CR and low
PRD values. In comparison to existing lossy transform domain techniques [25, 80], the
result obtained by the proposed technique shows high CR value and comparable PRD
values. The proposed lossy compression technique provides high CR value as redundant
and irrelevant information are removed in each stage by applying a series of compression
methods. In this method, though signal is compressed by removing first two IMFs
but information loss is very less. In other stages of signal compression, there is also
marginal loss of information occurs. In last stage, Huffman encoding is used so that no
loss of information occurs. Therefore overall CR value is high and the PRD values are
comparable with earlier reported techniques [25,80]. The comparison performance of the
proposed algorithm with other existing state-of- the-art techniques for the ECG record
49
Chapter 2. ECG Signal Compression and Decompression
Table 2.2: Performance of proposed compression algorithm
ECG Record
100
101
102
103
104
105
106
107
108
109
111
112
113
114
115
116
117
118
119
121
122
123
124
200
CR
24.11
23.26
23.81
23.28
23.85
23.82
23.81
23.27
24.39
23.89
24.15
23.87
23.81
25.00
23.43
23.56
24.46
23.85
23.81
23.83
23.96
24.10
23.26
23.81
PRD
1.31
0.87
1.07
2.38
1.12
0.48
1.90
1.90
0.32
0.56
0.65
0.73
2.53
0.86
2.87
3.84
1.72
2.90
1.71
0.18
0.99
2.37
1.65
0.86
ECG Record
201
202
203
205
207
208
209
210
212
213
214
215
217
219
220
221
222
223
228
230
231
232
233
234
CR
23.81
23.73
23.26
23.81
23.65
23.28
23.63
23.74
23.26
23.57
23.81
23.83
23.91
23.26
23.43
23.51
23.83
23.62
26.81
23.29
23.85
23.82
23.98
23.27
PRD
0.72
0.49
1.88
1.60
0.59
2.57
1.87
0.29
1.43
3.07
1.22
1.38
0.89
2.00
3.38
1.14
1.00
0.95
0.54
2.01
2.20
0.91
2.44
1.41
no. # 117 is presented in Table 2.3. The proposed compression method is also compared
with other algorithms presented by other researchers for the ECG record no. # 100, #
117 and # 119. The comparison details are given in Table 2.4. The algorithms presented
Table 2.3: Other algorithms comparison with proposed method for ECG record 117.
Compression Algorithm
AZTEC [26]
TP [27]
CORTES [28]
Fan [77]
SPHIT [80]
S. Lee et al. [25]
Proposed Algorithm
50
CR
10
2
4.8
3
8
24.4
24.46
PRD
28
5.3
7
4
1.18
1.17
1.72
Chapter 2. ECG Signal Compression and Decompression
Table 2.4: Performance comparison of different type ECG compression schemes
Algorithm
Lee et al. [32]
Tai et al. [94]
Chou et al. [95]
Eddie B.L et al. [96]
S. Lee et al. [25]
Proposed Algorithm
ECG Record 100
CR
PRD
24
8.1
10
1.48
24
4.06
24
3.95
23
1.94
24.11
1.31
ECG Record 117
CR
PRD
10
2.96
10
0.67
10
0.98
10
0.86
24.4
1.17
24.46
1.72
ECG Record 119
CR
PRD
12
5.7
20
2.17
10
1.03
10
0.93
19.3
2.05
23.81
1.71
by [25, 32, 94–96] for the ECG record no. # 100 reports the values of CRs 24:1, 23:1,
24:1, 24:1 and 10:1, respectively with corresponding PRDs 8.1, 1.94, 4.06, 3.95 and 1.48.
The proposed algorithm for the same record shows the CR value of 24.11:1 and PRD
value 1.31. The proposed technique shows better PRD value with a high value of CR.
The PRD value is very low which shows better result for exact reconstruction. Similarly
for the ECG record no. # 117 the algorithms presented by [25, 32, 94–96] reports CRs
values of 10:1, 24.4:1, 10:1, 10:1 and 10:1, respectively with corresponding PRDs of 2.96,
1.17, 0.98, 0.86 and 0.67. The proposed algorithm for the same record shows the CR
value of 24.46:1 and PRD value 1.72. Hence the proposed method presents high CR
value and comparable PRD value. The proposed technique is also compared with the
algorithms reported in [25, 32, 94–96] for the ECG record no. # 119, which shows the
values of CRs are 12:1, 19.3:1, 10:1, 10:1 and 20:1 respectively with corresponding PRDs
5.7, 2.05, 1.03, 0.93 and 2.17. The proposed algorithm shows the CR value of 23.81:1 and
PRD value 1.71 for the same ECG record. Similarly, the proposed compression method
is also compared with reported technique in [97] for the ECG record no. # 101, # 119,
# 210 and # 232. The comparison details are given in Table 2.5.
For ECG record no. # 101, # 119, # 210 and # 232 the reported technique in [97]
shows the values of CRs as 26.7:1, 23:1, 11.55:1 and 4.31:1 respectively and PRDs as 1.77,
1.95, 0.49 and 0.25 respectively. For same ECG records the proposed algorithm yields
the values of CRs as 23.26:1, 25.12:1, 23.74:1 and 23.82:1 and PRDs as 0.87, 1.7, 0.29 and
0.91 respectively. From these comparisons it is seen that except few records, the proposed
51
Chapter 2. ECG Signal Compression and Decompression
Table 2.5: Performance comparison of proposed method with Zahhad et al. method
ECG Record
101
119
210
232
Zahhad et al. [97] method
CR
PRD
26.7
1.77
23
1.95
11.55
0.49
4.31
0.25
Proposed Algorithm
CR
PRD
25.63
0.85
25.12
1.7
23.26
0.29
23.81
0.19
technique shows improved CR and PRD values as empirical mode decomposition (EMD)
based method data adaptive [85].
The proposed EMD based compression technique is also applied to real ECG signal
databases which are recorded from five volunteers using ADInstruments Power lab 26T
ECG machine and Labchart Pro software with 400 Hz and 1000 Hz sampling frequency.
The data is not associated with any particular age group. Experimental results shows
the values of CR and PRD as given in Table 2.6 and Table 2.7.
The average CR and
PRD are found to be 23.29:1 and 0.88, respectively for the ECG records shown in Table
2.6 whereas for ECG records of Table 2.7 the average CR and PRD are found to be
25.49:1 and 0.97 respectively.
Table 2.6: Performance evaluation of real time ECG signals recorded at 400Hz
Real ECG Record
Volunteer 1
Volunteer 2
Volunteer 3
Volunteer 4
Volunteer 5
CR
17.86
23.26
18.18
28.57
28.57
PRD
0.66
0.99
1.12
0.57
1.08
Table 2.7: Performance evaluation of real time ECG signals recorded at 1000Hz
Real ECG Record
Volunteer 1
Volunteer 2
Volunteer 3
Volunteer 4
Volunteer 5
52
CR
25.69
28.57
30.3
24.39
18.52
PRD
1.07
0.83
0.53
1.24
1.18
Chapter 2. ECG Signal Compression and Decompression
The proposed EMD based compression technique decomposes the signal into a series
of IMFs. The first two IMFs (mostly noise components) are removed to get highly
compressed data. The removal of redundant and irrelevant information in each stage
further increases the CR values. Though there is marginal loss of information in each
stage but Huffman encoding preserves all the information without any data loss. The
Huffman coded compressed data upon reconstruction provides the PRD values which
are comparable with earlier reported methods.
2.6
Summary
This chapter presents a new technique for ECG signal compression based on empirical
mode decomposition (EMD). First, EMD technique is applied on ECG signal to decompose it in several intrinsic mode functions (IMFs). Next, downsampling, discrete
cosine transform (DCT), window filtering and Huffman encoding techniques are used
sequentially to all IMFs for compressing the ECG signal. The reconstruction method
consists of Huffman decoding, inverse discrete cosine transform (IDCT) and spline interpolation. The proposed algorithm is compared by evaluating all 48 ECG records of
MIT-BIH arrhythmia database in terms of compression ratio (CR) and the reproduction
(after reconstruction) efficacy in terms of percent root mean square difference (PRD).
The average values of CR and PRD are found to be 23.74:1 and 1.49, respectively. The
proposed compression algorithm is also evaluated using European ST-T data base. The
average CR value is found to be 23.5:1 for the selected ECG records of European ST-T
data base. The compressed data obtained using European ST-T database are used for
transmission over wireless medium using a GSM modem. Here it can be mentioned that
the PRD for the European ST-T data base is not calculated at this point of time and will
be calculated after reconstructing the original signal from the transmitted compressed
data. This compression performance facilitates transmission of ECG data using a GSM
modem and calculation of PRD, will be discussed in chapter 3.
53
CHAPTER 3
TRANSMISSION OF COMPRESSED
ECG USING SMS
3.1
Introduction
ECG, an important physiological signal is used as a diagnostic tool for cardiac patient
monitoring [98]. Use of computer based advanced technologies can provide faster diagnosis of cardiac patient. Signal processing applications facilitate for analysis and transfer
of data from point of measurement to the physical higher level health care facility. Advances in mobile communication technology can aid to establish faster health care by
means of easy transfer of medical data, along with advance infrastructure to provide advance health care [99]. In medical science, this process of transmission of medical data
using telecommunication medium is termed as ‘telemedicine’. Use of mobile devices
has the potential to improve the flexibility in cardiac health monitoring [5]. Electronic
devices like Mobile phones, general packet radio service (GPRS), global system for mobile communications (GSM) modems allows computers to communicate over wireless
medium [100]. Transmission of ECG data from a patient using wireless medium, is difficult, as volume of ECG data is large. In cardiac patients the ECG data is of the order
of GB. So for efficient transmission the bulky data need to be compressed sufficiently.
The compressed data then can be easily transmitted using GSM modem based advanced
54
Chapter 3. Transmission of compressed ECG using SMS
electronic devices [101].
Mobile phones, GPRS, GSM modems use wireless technology to transmit data over a
long distances in the form of short message service (SMS), multimedia messaging service
(MMS), GPRS data, etc. GSM/GPRS modem (USB dongle or mobile phone) can be
easily connected to computer. Transmission of data through SMS is a cost effective
off-line process [23]. This work is aimed for remote health monitoring in a typical rural
area. Here, the rural area is presumed to be deprived of internet connectivity. Another
assumption is that the 2G mobile communication service is available in the rural area.
Here, the compressed ECG signal is transmitted by SMS over wireless medium. A GSM
modem is used as a signal transmitter and a GSM mobile phone is used as SMS receiver.
3.2
SMS based data transmission
Short message service (SMS) is a text messaging service provided by telecom operators
[47]. These SMS can be created by mobile phones or other mobile assisted devices (e.g.:
personal computers) and devices can send or receive SMS messages by communicating
with the telecom network. The advantage of SMS is that text messaging is supported
in all languages internationally and is supported by all mobile operators and mobile
phones. Currently inexpensive SMS subscription plan is provided by almost all mobile
service providers. SMS provides flexibility in sending and receiving text messages over
GSM network. It is also suitable for any form of mobile phone. These advantages of
extremely low cost SMS service can be utilized in mobile based health care systems.
GSM technology supports various ways for SMS transmission. SMS message is popular
because of some special facilities [22], which are listed as follows.
• SMS can be send and read at any time from anywhere.
• SMS can be send to switched off Mobile Phone.
• SMS is less disturbing and noisy, unlike voice call.
• SMS supports all GSM mobile phones irrespective of mobile service provide.
• SMS subscription plans are less expensive.
55
Chapter 3. Transmission of compressed ECG using SMS
• SMS is a suitable technology to build wireless applications.
• SMS service is available at all places supported by mobile phone.
In wireless tele-cardiology application, the cardiac signal can be transmitted by efficient
compression through computer based programming. SMS can be suitable for exchanging
medical data between a patient and a physician or a doctor.
3.2.1
Computer based SMS transmission
With advancement in technology, computer plays an important role to establish communication in every field. The field of communication may be at hospitals or industry
or any organization. Computer provides fast and efficient computation, there by saving
time, money and resources. The available methods of communication through e-mail,
mobile phone, fax, etc., can be used with computers for technological benefits. Using
mobile phone or GSM modem with computer, information can be easily sent in the form
of SMS, which is the popular messaging service and is economical. Two popular ways to
send or receive SMS from a computer or PC [47] are described as follows.
I. SMS supported hardware connected to a computer.
II. IP SMS connection through SMS Center (SMSC).
3.2.1.1
Sending of SMS from a PC using wireless modem, mobile phone or
USB dongle
Computer can send SMS through a wireless modem (or mobile phone) with a valid
Subscriber Identity Module (SIM) card. A communication link is established between the
wireless modem and GSM network with the aid of SIM card. The wireless modems can
be connected to PC in different ways. The modems or mobile phones can be connected
to PC through a USB port or a bluetooth link or data cable. Following this a standard
set of AT-commands are used to instruct the wireless device or GSM/GPRS modem to
send SMS messages through computer. Wireless modems also supports some extended
set of AT commands (described in previous section) to control SMS message sending
and receiving. The advantage is that the SMS sending process is its low cost, off line
56
Chapter 3. Transmission of compressed ECG using SMS
(no need of internet) service and easy to set up. The wireless connection to a computer
using modem is shown in Figure 3.1.
SMS
Wireless Link
Computer
SMS Center
GSM Modem
Mobile Phone
Figure 3.1: Wireless modem connection to a computer
3.2.1.2
Sending SMS from a PC using IP connection
Another way of SMS transmission is through the aid of a SMS center where the PC can
be connected directly to SMSC or SMS gateway of the GSM service provider over the
internet. IP SMS connections can be made using TCP/IP. This type of communication
facilitates large number of SMS messages in a short time as it has a better bandwidth as
compared to GSM modem. However, this service requires internet connection and may
not be available in rural area.
3.2.2
Brief introduction on AT-commands
Wireless modems are controlled by the instructions known as AT-commands. Many
wired dial-up modems supports some basic AT-commands that are also supported by
GSM/GPRS modems and mobile phones. The basic AT-commands are ATD (Dial), ATA
(Answer), ATH (Hook control) and ATO (Return to online data state). Except these
basic AT-commands, GSM/GPRS modems and mobile phones support some extended
AT-commands specific for GSM technology. The extended AT-command line generally
begins with ‘AT’ followed by ‘+’. The prefix ‘AT’ or ‘at’ informs the wireless modem
regarding the beginning of command line. Full set of GSM supports extended ATcommands is available at [47]. Some of the basic operations performed by AT commands
are described as follows.
57
Chapter 3. Transmission of compressed ECG using SMS
• AT-commands AT+CGMI, AT+CGMM, AT+CGSN and AT+CGMR provide the
basic information like name of manufacturer, model number, IMEI (International
Mobile Equipment Identity) number and software version, respectively for the
mobile phone or GSM/GPRS modem.
• A subscriber’s information like MSISDN (Mobile Station International Subscriber
Directory Number) and IMSI (International Mobile Subscriber Identity) number
can be obtained using the AT-commands AT+CNUM and AT+CIMI respectively.
• AT-commands also provide the current activity status of the mobile phone or
GSM/GPRS modem.
• The sending and receiving of SMS as well as read, write or searching of phone book
is posssible using AT-command.
• The security-related tasks like SIM lock, phone lock also performed by AT-commands.
• Change in the configurations of the mobile phone or GSM/GPRS modem like GSM
network, SMS center address are controlled by AT-commands.
3.3
Proposed Framework
The methodology proposed here for transmission and reconstruction of ECG signal from
the received SMS data are described in the following sections. For tele-cardiology application, the proposed framework is divided into two parts.
I. Wireless transmission of compressed ECG.
II. ECG signal reconstruction from the received SMS data.
3.3.1
Methodology for wireless transmission of compressed ECG
The methodology followed for GSM modem based wireless transmission of compressed
ECG data consists of following steps.
• Interfacing of GSM modem with PC
• GSM modem access in PC
• Testing of GSM modem for SMS transmission
58
Chapter 3. Transmission of compressed ECG using SMS
• Transmission of ECG
The work setup for the ECG signal transmission is shown in Figure 3.2.
USB GSM Modem
Compressed
ECG data
Figure 3.2: Setup for wireless ECG transmitter
3.3.1.1
Interfacing of GSM modem with PC
For experimental purpose, a 3G USB Modem ZTE data card MF 190 was used. Use
of USB standard interface establishes easy plug-and-play connection of modem to any
computer. Plugging the USB modem into the USB port, the modem is automatically
detected and installed in PC. No external power is required to drive the modem as it
draws power from the USB connection. In windows 7, data communication through
serial port of the computer is made by the processes as follows (presume USB modem
is plugged in).
Open the Control Panel ⇒ Click on Hardware and Sound ⇒ Click on Device Manager
⇒ Open Modems option ⇒ Click on ZTE Proprietary USB modem ⇒ Then click on the
modem option to identify the COM port number at which GSM modem is connected.
3.3.1.2
GSM modem access in PC
To access a GSM modem in computer, test commands are used to check whether ATcommands are supported by the modem. The test command ‘AT’ checks the communication between GSM modem and PC. The real time work setup for transmission of
compressed ECG is shown in Figure 3.3.
59
Chapter 3. Transmission of compressed ECG using SMS
Figure 3.3: Real time work setup for wireless ECG transmission
3.3.1.3
Testing of GSM modem for SMS transmission
The command ‘ATI’ is used for controlling a GSM phone or modem and provides the
status of the modem (i.e., Manufacturer, Model No., Revision, IMEI, other capabilities).
For an example,
ATI
Manufacturer: ZTE CORPORATION
Model: MF190
Revision: BD_RELIANCEMF190V1.0.0B01
IMEI: 911133908272823
+GCAP: +CGSM,+DS,+ES
OK
The return code ‘OK’ indicates that ‘ATI’ command is executed successfully and modem
is initialized.
60
Chapter 3. Transmission of compressed ECG using SMS
3.3.1.4
SMS Transmission
The GSM modem uses the AT-command ‘+CMGS’ to send a SMS message to a phone
number. A mobile phone or GSM modem operates SMS in two modes, either text or
PDU (Protocol Data Unit) mode. The text mode of SMS is easier to operate. First
the AT-command ‘+CMGF’ is used to set the SMS mode. The values ‘1’ and ‘0’ refer
to SMS text mode and PDU mode respectively [102]. For an example, text mode SMS
follows.
AT+CMGF=1
OK
The command line ‘AT+CMGF=1’ instructs the modem to operate in SMS text mode.
The return result code ‘OK’ indicates that ‘+CMGF’ command is executed successfully.
If the operating mode is not supported by the GSM modem then ‘ERROR’ will be
returned. Finally the command line ‘AT+CMGS’ instructs for sending a text message
from a computer to a mobile phone number using GSM modem. An algorithm for SMS
transmission process is represented in Algorithm 1.
Algorithm 1 SMS transmission using GSM modem
1: Load the compressed ECG data array ‘x’.
2: X = strconv(x);
% Integer to string conversion
3: j = 0
4: for i = 0 : strlen(X)
5:
if j < 160 then
6:
T x_data(j) = X(i);
7: j + +;
8:
else
9:
sendSM S(T x_data);
10: j = 0;
% reset counter
11:
end if
12:
i + +;
13: end for
A single SMS message can contain maximum of 140 bytes (1120 bits) of data, this
means it can contain up to 160 characters if 7-bit (ASCII) character encoding is used.
Each data point of a compressed ECG signal is separated using semicolon (;) delimiter
(ASCII code is 59) before SMS transmission. Thus a concatenated SMS text message
61
Chapter 3. Transmission of compressed ECG using SMS
can be send by breaking a message into smaller parts where each of these parts are fitted
into a single SMS message and sent to the recipient’s mobile phone. For an example, the
compressed ECG data obtained from first 1000 samples of European ST-T ECG record
no. # e0613 are transmitted as multiple SMS. The ECG was broken into eight number
of SMS messages.
3.3.2
Methodology for ECG signal reconstruction
The methodology followed for reconstruction of ECG signal from the received SMS
consists of following steps.
• Transferring of multiple SMS from a mobile phone to PC.
• PC based SMS joining to reconstruct the full ECG.
The received compressed ECG data was decompressed using MATLAB software. The
work setup for the ECG signal reconstruction is shown in Figure 3.4.
SMS
MATLAB based
data
decompression
Reconstructed
ECG
Figure 3.4: Setup for ECG signal reconstruction
3.3.2.1
Transferring of multiple SMS from a mobile phone to PC
Concatenated text messages are received in the mobile phone at the receiver. This text
messages contain multiple SMS and each symbol in a SMS is delimited by semicolon. As
an example, first three received SMS messages are shown in Figure 3.5. After receiving
all SMS, these text messages are transferred to a PC or computer host via blue-tooth or
data cable for further processing.
62
Chapter 3. Transmission of compressed ECG using SMS
Figure 3.5: Received SMS messages
3.3.2.2
Reconstruction using SMS joining
The received multiple SMS messages are joint to form single text message. These text
messages are then converted to unsigned integer of 16 data type representation. Following this the unsigned data are further processed to reconstruct the original signal
using the decompression technique as discussed in Chapter 2. Text data after converted
to unsigned integer 16 data type, are fed as input to the decompression block. The
decompression is carried out through Huffman decoding. Inside the Huffman decoding
block, a Huffman to normal data compression algorithm decompresses the data. The
decompressed data is then interpolated through cubic- spline interpolation to get back
the original ECG signal. The work flow diagram of PC based ECG signal decompression
from the received SMS message is shown in Figure 3.6.
63
Chapter 3. Transmission of compressed ECG using SMS
Received
Text message
Huffman Decoding
Inverse DCT
Spline
Interpolation
Reconstructed
ECG Data
Figure 3.6: Flow chart for reconstruction of ECG signal
3.4
Experimental Results and Discussions
As mentioned in previous chapter, ECG records from European ST-T database [6] are
used for data compression. The compressed ECG data are then transmitted over wireless
medium for remote patient monitoring. For an example, in this experiment, first 1000
samples of ECG record no. # e0613 from European ST-T database are transmitted
by splitting the message into SMS. The SMS messages are sent as concatenated SMS
messages. The concatenated SMS dispatch process is presented as screenshot in Figure
3.7. The effectiveness of the decompression technique to reconstruct the original signal
is evaluated using the ECG records of European ST-T database. The performance
parameter, percent RMS difference (PRD) is evaluated using the (2.4). The experimental
values of PRD for European ST-T database are given in Table 3.1. In Figure 3.8 the
original ECG signal and the reconstructed signal were plotted. A magnified version of
a part of the plot (represented by straight line) is shown in the small window. From
the Figure 3.8, it is evident that the ECG signal was reconstructed with less error. The
difference between the original ECG signal and reconstructed ECG signal, error, was
64
Chapter 3. Transmission of compressed ECG using SMS
Figure 3.7: The SMS based ECG transmitter output
Table 3.1: Evaluation of PRD for European ST-T database
ECG Record
e0105
e0112
e0122
e0127
e0207
e0211
e0404
e0417
e0606
e0613
e0615
e0704
PRD
0.71
6.92
0.384
0.56
4.22
2.31
0.1
0.243
0.13
0.22
0.46
0.24
evaluated. The error square (ε2 ) signal was calculated as
ε2 = [ (Original ECG) − (Reconstructed ECG) ]2
Figure 3.9 represents the normalised error square (in dB). It is seen from the Figure 3.9,
that the error square is marginal.
65
Chapter 3. Transmission of compressed ECG using SMS
−500
Reconstructed signal
Original ECG signal
Amplitude
(mV)
−550
−850
−600
−900
−650
−950
480 500 520 540 560
−700
−750
−800
−850
−900
−950
−1000
0
200
400
600
No. of samples
800
1000
Figure 3.8: Original and reconstructed ECG signal
0
−10
Normalised ε2
(dB)
−20
−30
−40
−50
−60
−70
0
200
400
600
800
1000
Samples
Figure 3.9: Normalised error square signal in dB
3.5
Summary
This chapter presents, a technique for transmission of compressed ECG data using wireless medium and also reconstructs the ECG signal from received data. For transmission
purpose, a USB GSM modem is used with computer to send the compressed ECG signal
as SMS message. Modem is controlled by AT-command in a PC. The compressed data
used is the output obtained from the previous chapter based on Empirical Mode Decomposition (EMD). All the SMS messages are received successfully in a mobile phone
used for this purpose at receiver section. The received SMS messages are transferred to
PC using blue-tooth and the data decompression algorithm is carried out using MATLAB software to reconstruct the ECG signal. The reconstructed ECG signal is then
compared with the original ECG signal and an error signal is found. A reconstruction
66
Chapter 3. Transmission of compressed ECG using SMS
parameter PRD is used to evaluate this technique. The average PRD is found to be
1.38 for selected records of European ST-T database. The low value of PRD means the
reconstructed signal contains less error. Hereafter the reconstructed ECG signal will be
further processed for detection of cardiac disorders. Detection of cardiac disorders from
the received signal is presented in Chapter 4.
67
CHAPTER 4
DETECTION OF CARDIAC
DISORDERS LIKE BRADYCARDIA,
TACHYCARDIA AND ISCHEMIA
4.1
Introduction
Determination of different wave peaks in the ECG signal are important for detection
of cardiac disorders. A full cycle of ECG signal is generated in every heartbeat. The
normal rhythmic contraction and expansion of the arteries inside the human body is
known as heartbeat. One of the important parameters to evaluate a person’ s health
is the heart rhythm. Heart rhythm defines the speed of the heartbeat and is useful
for heart rate (HR) calculation. The heart rate is the number of heartbeats per unit
of time. Generally HR is measured in beats per minute (bpm). A normal person is
identified by the heart rhythm and HR calculation. The abnormal heart rhythm causes
slow or fast heartbeat [15]. Early detection of such abnormal heart rhythm is most
important to avoid serious cardiac disorders. The HR value is calculated and compared
with normal HR range to identify various heart rhythm abnormalities like bradycardia
and tachycardia. The heart rate below and above normal range are called bradycardia
and tachycardia respectively. Another common heart disorder which causes heart attack
68
Chapter 4. Detection of cardiac disorders like bradycardia, tachycardia and ischemia
is known as ischemia or cardiac ischemia. Ischemia or heart stroke is a cardiovascular
disorder which affects the heart and the blood vessels. Here, the coronary arteries
become narrowed by atherosclerosis which restricts the flow of blood and oxygen to the
heart. This can lead to brain cells to die which can creates a cardiac disorder known as
ischemia [103]. Detection of ischemia takes long time to analyze.
Many techniques have been developed for detection of cardiac disorders in [16,50,61,
63, 104, 105]. The cardiac dysrhythmia techniques [50, 52, 53, 104] fails to provide more
analysis on the information content in the ECG signal for complete diagnosis. The technique reported [52, 55, 56] uses PCA, NN classifier, etc., which adds complexity to the
system. The ischemia detection techniques [16, 55, 61, 106] takes more computation time
to estimate the ST-segment and or T-wave end points for diagnosis of ischemic disorder.
The detection process takes more time if analyzed by doctor using long duration ECG
data. So an automatic technique is required for quick detection of cardiac disorders.
The location of ECG wave peaks are required for detail analysis and automatic diagnosis of cardiac dysrhythmia. The ECG beat classification is essential for automatic
detection and diagnosis of ischemic episodes in a long duration electrocardiogram. The
key to ischemic episodes detection is the ST-segment deviation and T-wave amplitude
changes [107]. Most importantly the parameter, ST-segment deviation is expressed as
polarity change relative to isoelectric line. The isoelectric line is the baseline, typically
measured between the T-wave offset and the preceding P-wave onset of electrocardiogram. Isoelectric line is used as a reference for measurement of ST-segment deviation
and T-wave amplitude changes [108].
In the preceding chapter, the process of transmission of compressed ECG signal
was demonstrated. The reconstruction of compressed ECG signal from its compressed
samples was successfully demonstrated. The reconstruction process is done by the doctor
and hence it is possible to analyze the ECG data for detection of cardiac abnormalities.
This chapter describes the process of cardiac abnormality detection.
69
Chapter 4. Detection of cardiac disorders like bradycardia, tachycardia and ischemia
4.2
Proposed framework
The proposed work consists of five stages [109]. The block diagram of overall process
is shown in Figure 4.1. In first stage, ECG recording is pre-processed to reduce noise
components and QRS-complexes are detected by filtering. In the next stage, consecutive
QRS-complexes are used to detect heart rhythm abnormality. Thereafter, ECG feature
extraction is carried out to locate other ECG wave peaks, ST-segment and T-wave. In
the next stage, beat classification is done as normal or ischemic using certain rule, based
on medical knowledge and the final stage provides the identification of ischemic episode
which is based on the detection of two or more consecutive ischemic windows using first
30s of each ECG recording.
ECG Data
Preprocessing
Feature
Extraction
Beat
Classification
Ischemic Episode
Recognition
Cardiac
Dysrhythmia
Detection
Figure 4.1: Block diagram for cardiac disorder detection
The complete flow diagram of proposed cardiac disease detection process is given in
Figure 4.2.
4.2.1
Preprocessing
Pre-processing of raw ECG signal is required for removal of noises consisting of muscle
noise, baseline wander and T-wave interference, etc., [110]. The P- and T-wave frequency
generally lies between 0.5Hz and 10Hz. Sometimes these frequency coincides with the
baseline noise having a low frequency range of 0-0.8Hz [1]. Hence, it is important to
remove the baseline noise for true peak detection in ECG signal. ECG signal is first
amplitude normalized and then band pass filter (5-15Hz) is used to reduce the effect
of noises. The band pass filter is composed of cascaded high-pass and low-pass integer
filters [111]. The functionality of pre-processing stage is elaborated in following sections.
70
Chapter 4. Detection of cardiac disorders like bradycardia, tachycardia and ischemia
Raw ECG
signal
Pre -processing
Heart rhythm
abnormality detected
Heart Rate
calculation
Feature extraction
Is
ischemic beat
classification rule
satisfied
?
No
Beat is normal
Yes
Is
ischemic
window condition
satisfied
?
No
Window is normal
Yes
Is
ischemic
episode condition
satisfied
?
No
Episode not found
Yes
Ischemic episodes
detected
End
Figure 4.2: Complete flow diagram of proposed framework
71
Chapter 4. Detection of cardiac disorders like bradycardia, tachycardia and ischemia
4.2.1.1
QRS-complex detection
In general the term heart beat refers to the entire ECG cycle of PQRST, but the QRScomplex represents the instant that a beat occurs. The QRS-complex portion of the
ECG is the most distinctive feature for easy cardiac disorder identification. The duration
of the QRS-complex is normally less than or equal to 100ms [9]. R-peak is the most
prominent and the tallest peak in determination of QRS-complex. The detail processes
for filtering and QRS-complex approximation is based on algorithm by J. Pan and J.
Tompkins [111]. The block diagram of QRS-complex detection process used by Pan and
Tompkins algorithm [111] is presented in Figure 4.3.
Normalized
ECG
Band pass
Filter
d
dt
2
Moving Window
Intergration
QRS complex
Detection
Figure 4.3: Stages of QRS-complex detection
Step 1: Amplitude normalized ECG signal is filtered using a band pass integer filter.
The desirable pass-band to maximize the QRS energy is approximately 5-15Hz [111].
Step 2: The band pass filtered signal is differentiated for finding high slopes which
normally distinguishes QRS-complex from other ECG waves.
Step 3: The differentiated signal is squared point by point to make all the data points
positive and does the nonlinear amplification to emphasize the higher frequency (i.e.
ECG frequencies) in the differentiated signal.
Step 4: The squared waveform then passes through a moving window integrator to
obtain waveform feature information in addition to the slope of the R-wave. The width
of window is chosen to be long enough to include the widest QRS-complex.
Step 5: The thresholds are calculated using running estimate of signal peak and noise
peak. The thresholds are automatically adjusted to overcome the noise peak and QRScomplex is detected.
The input ECG signal and signals after normalization are shown in Figure 4.4. The
filtered ECG signal after differentiation and location of RR-interval is shown in Figure
72
Chapter 4. Detection of cardiac disorders like bradycardia, tachycardia and ischemia
4.5.
−400
Amplitude (mV)
Input ECG Signal
−600
−800
−1000
0
100
200
300
400
500
600
No. of samples
700
800
900
1000
1
Amplitude (V)
Normalized Signal
0.5
0
−0.5
0
100
200
300
400
500
600
No. of samples
700
800
900
1000
Figure 4.4: Different signals during filtering
(a) ECG signal (European ST-T record tape no. # e0613) (b) Normalized signal
4.2.1.2
RR-interval determination
R-peak is the tallest peak in QRS-complex. RR-interval is determined by finding the
time difference between two consecutive R-peaks [112].
4.2.1.3
Heart Rate calculation
The heart rate (HR) is calculated from the extracted features of ECG signal by finding
the inverse of RR-interval. HR is expressed in beats per minute (bpm) and the normal
range of HR is 60-100 bpm. The formula used to calculate heart rate is
where trr =
RR−interval(in samples)
sampling f requency .
60
trr
bpm [1],
Here the sampling frequency of 250Hz is used as per
European ST-T database [6]. Hence HR value is calculated as
250
RR−interval (in samples) ∗60
beats/min. The HR value is used to identify slow or fast heart rhythm.
4.2.2
Heart rhythm abnormality detection
The calculated heart rate value is compared with normal range to detect the heart
rhythm abnormalities like bradycardia and tachycardia. The fast or slow heart rhythm
73
Chapter 4. Detection of cardiac disorders like bradycardia, tachycardia and ischemia
0.8
R
R
R
RR interval
0.6
RR interval
0.4
0.0
(V )
Amplitude
0.2
-0.2
Q
Q
Q
-0.4
-0.6
-0.8
0
S
200
S
400
S
600
800
1000
No. of samples
Figure 4.5: RR-interval determination using QRS-complex peaks
is identified by calculating HR value. The HR value is less than 60 bpm then it is termed
as slow heart rhythm or bradycardia and if HR is greater than 100 bpm then it is called
as fast heart rhythm or tachycardia [15].
4.2.3
Feature extraction
The ECG features for ischemia detection is determination of ST-segment and T-wave
alternation from QRS-complex. Pan and Tompkins algorithm [111] is one of the most
popular algorithm to find QRS-complex. Other features like P-wave location, J-point
location, T-wave, TON and TOF F locations, isoelectric line and ST-segment location are
extracted by using the previously located Q-, R- and S-wave peaks.
4.2.3.1
P-wave Location
A threshold level is used with reference to the normal range of ECG segments to locate Pwave. The normal PR-interval ranges from 120-200ms whereas amplitude and duration
74
Chapter 4. Detection of cardiac disorders like bradycardia, tachycardia and ischemia
of P-wave are 0.25mV and 80ms respectively [1].
4.2.3.2
J-point Location
The J-point is the junction between the QRS-complex and the ST-segment of ECG
signals [108]. It is also the first point where the waveform flattens out to the right after
QRS-complex. The J-point location is normally at the end of QRS-complex which has
normal range of 80-120ms.
4.2.3.3
T-wave, TON and TOF F Location
After determining the location of R-peak and J-point, the peak of T-wave is estimated
as maximum elevation between R-peak + 400ms and J-point + 80ms. TON and TOF F
is then estimated by considering 35ms duration from left and right of the T-wave peak
respectively [1].
4.2.3.4
Isoelectric Line and ST-segment Location
Isoelectric line is the baseline or almost zero amplitude level. The base line is chosen as
the flat line between P-wave and Q-wave. The location for isoelectric line was estimated
by finding the start and end point of all zero slope amplitude ECG level. All the extracted
ECG features are as shown in Figure 4.6. ST-segment is located 80ms after J-point when
cardiac rhythm is less than 120 bpm and 60ms after J-point when the cardiac rhythm
is more than 120 bpm [113].
4.2.4
Ischemic beat classification
The ST-segment and T-wave are the two features generally used by cardiologist for
ischemic beat classification. The beat classification is based on clinical rules as reported
in [105]. The rules considers as, the beat is ischemic when ST-deviation is more than
0.08mV above or below the isoelectric line [114] and the beat is ischemic when T-wave
is inverted or flattened [105]. The T-wave inversion is measured considering T-wave
amplitude variation (positive or negative) with respect to the isoelectric line for first 30s
75
Chapter 4. Detection of cardiac disorders like bradycardia, tachycardia and ischemia
0.8
R
R
R
RR interval
0.6
0.4
Isoelectric line
J
J
P
(mV)
Amplitude
0.2
P
T
T
P
T
OFF
OFF
OFF
0.0
T
T
T
ON
ON
-0.2
J
Q
ON
Q
Q
-0.4
-0.6
-0.8
0
S
200
S
400
S
600
800
1000
No. of samples
Figure 4.6: ECG signal extracted wave peaks and points with baseline
duration ECG. The ischemic beat classification process is represented in Algorithm 2 as
per the reported technique in [105].
Algorithm 2
Ischemic beat classification
if (ST- segment ≤ 0.08mV) (or) (ST- segment ≥ 0.08mV) (or) (T inverted or T→
0mV) then
The beat is ischemic
else The beat is normal
end if
4.2.5
Ischemic episode recognition
As per the recommendation of ESC (European Society of cardiology) the ischemic
episode detection procedure considers minimum 30s duration of signal. The ischemic
episode detection process considers a sliding window technique which searches the sequences of ischemic beats exist for 30s or more. The first sliding window includes first
30s of the signal and the technique proceeds moving the window one beat at a time
keeping window duration of 30s. Normally a threshold value of 75% criteria detects
ischemic windows [105]. The ischemic window is detected if the 30s window contains
76
Chapter 4. Detection of cardiac disorders like bradycardia, tachycardia and ischemia
more or equal to 75% of ischemic beats [105]. Ischemic episode recognition is the process
to identify consecutive 30s windows and 75% of the 30s window must have ischemic
beats [115]. The ischemic episode left boundary corresponds to the beginning of the
first window in the series and the right boundary corresponds to the end of the last
window [105]. The ischemic episode detection process is represented in Algorithm 3 as
per the reported technique in [105].
Algorithm 3
Ischemic episode detection
if ([(No. of Ischemic beats) /(All beats)] ≥ 0.75) then
The window is ischemic
else The window is normal
end if
if (No. of consecutive ischemic window ≥ 2) then
Ischemic episode is identified
end if
4.3
Experimental Results and Discussions
The effectiveness of this technique is evaluated using ECG records from European ST-T
database [6]. The heart rhythm abnormalities i.e., bradycardia and tachycardia are detected by comparing the calculated HR value with the normal range of heart rate. The
results for cardiac dysrhythmia detection using European ST-T database is presented in
Table 4.1. Ischemic episode detection performance is evaluated in terms of the parameters sensitivity (Se) and positive predictive accuracy (PPA). The sensitivity measures
the ability to detect ischemic episode where as PPA gives estimation likelihood that a
detected episode is a true ischemic episode [116]. These parameters are evaluated as:
Se =
TP
× 100
TP + FN
PPA =
TP
× 100
TP + FP
where, TP = True Positives (Correctly detected event), FP = False Positives (Erroneously detected non event), FN = False Negatives (Erroneously missed event) As mentioned above, TP represents the annotated beats/episodes in the database, FN corresponds to the annotated beats/episodes that were not detected and, finally, FP denotes
77
Chapter 4. Detection of cardiac disorders like bradycardia, tachycardia and ischemia
Table 4.1: Heart rhythm abnormalities identification
ECG
Data
e0105
e0112
e0122
e0127
e0207
e0211
e0404
e0417
e0606
e0613
e0615
e0704
RR- interval
(ms)
826
748
593.3
731
756
436.5
794
616.8
661
849.3
718
688
HR
(bpm)
73
80
101
82
79
137
76
97
91
71
84
87
Heart rhythm abnormality
Normal heart
Normal heart
Tachycardia
Normal heart
Normal heart
Tachycardia
Normal heart
Normal heart
Normal heart
Normal heart
Normal heart
Normal heart
rhythm
rhythm
rhythm
rhythm
rhythm
rhythm
rhythm
rhythm
rhythm
rhythm
the number of beats/episodes that were not annotated in the database, but that were
incorrectly identified by the algorithms [103]. The correctly detected or true positive
events are the reference annotations available in European ST-T data base. The ischemic episode detection results are presented in Table 4.2.
Table 4.2: Results of ischemic episode detection
ECG Number
Data of
30s
windows
Number
of
Ischemic
windows
Original
No.
of
Ischemic
ST
episodes
e0105
e0112
e0122
e0127
e0207
e0404
e0417
e0606
e0613
e0615
e0704
694
36
2077
316
14
30
31
4381
465
3811
5368
11
5
2
7
6
5
3
2
11
3
3
3693
3672
6245
5053
3815
3884
5188
4415
4097
3832
5376
Number
of
Ischemic
ST
Episodes
detected
8
6
3
6
3
3
3
3
5
3
3
TP
FP
FN % Se
% PPA
8
5
2
6
3
3
3
2
5
3
3
0
1
1
0
0
0
0
1
0
0
0
3
0
0
1
3
2
0
0
6
0
0
100
83.33
66.67
100
100
100
100
66.67
100
100
100
72.73
100
100
85.71
50
60
100
100
45.45
100
100
From the result it is seen that, this methodology detects 3693 number of 30s windows,
694 number of ischemic windows and 08 numbers of ST episodes for the ECG record78
Chapter 4. Detection of cardiac disorders like bradycardia, tachycardia and ischemia
ing e0105. The number of available ischemic ST episodes is 11 as per the European
ST-T database. This technique achieves an average sensitivity and positive predictive
accuracy of 83.08% and 92.42% respectively. The performance of the other reported
algorithms are also discussed like, Silipo et al. [114] presents an algorithm for ischemic
episode detection which could determine average sensitivity of 76% and average positive
predictive accuracy of 85% using a recursive neural network. Vila et al. [117] uses fuzzy
approach for the detection of ischemic episodes, when the average sensitivity and average positive predictive accuracy parameters were of 84% and 90%, respectively. The
algorithm presented by Maglaveras et al. [55] detects average ischemic episode detection
sensitivity is 88.62% while positive predictive accuracy is 78.38% using an adaptive back
propagation neural network. Use of Hidden Markov Model for ischemic episodes detection was introduced by Andreao et al. [118] that achieved an average sensitivity of 83%
and a average positive predictive accuracy of 85%. Most of the earlier reported techniques [55,114,117] are based on classifiers which makes computation more complex. For
timely detection, an automatic technique is used for ischemic episode detection and it
provides good accuracy in terms of average sensitivity and positive predictive accuracy.
4.4
Summary
The proposed technique first evaluates the heart rate. The predefined normal HR range
is used to identify slow or fast heart rhythm. The abnormal heart rhythm i.e., bradycardia and tachycardia are detected by this process. The ischemia detection technique finds
the consecutive 30s ischemic windows to identify ischemic episodes. The performance
measurement parameters are calculated using European ST-T database. The performance of this technique improves in terms of average sensitivity and average positive
predictive accuracy and it is practically useful for diagnosis of other diseases. The inclusion of heart dysrhythmia identification to ischemic episode detection technique provides
an improved diagnostic tool for an automated cardiovascular disease detection system.
79
CHAPTER 5
CONCLUSION AND FUTURE SCOPE
OF WORK
This thesis presents a novel technique for ECG signal compression so that it can be
sent over wireless network using a set of SMS. The compression is based on empirical
mode decomposition (EMD). The high adaptability of EMD makes it popular in nonstationary signal processing. Efficient and economical transmission of compressed signal
using wireless technology has been demonstrated. Here, ECG signal after compression
is transmitted as a series of SMS. A mobile phone can be used to receive the transmitted
text data and subsequently the decompression process is carried out to reconstruct the
ECG data. The physician or the doctor at a hospital can analyse the reconstructed signal
for detection of cardiac disorders like heart rhythm abnormalities and ischemic episodes.
The real time implement of this mobile health care system may be useful for reliable
and economical diagnosis of cardiac patients located at remote areas. The experimental
studies reported in this thesis are briefly summarized in the following sections. All the
algorithms presented in the thesis have been implemented using MATLAB Version 7.10,
Release name R2010a, Number 23 of Mathworks Inc. All experiments are carried out
on a single computer having Intel Core i5 computer, processor 3.20Ghz with 4GB RAM
and Windows 7 operating system.
80
Chapter 5. Conclusion and Future Scope of Work
5.1
Conclusions
• Empirical mode decomposition (EMD) technique was used in this thesis to compress the ECG signal. EMD technique first decomposes the ECG signal into several intrinsic mode functions (IMFs). The last IMF is called the residue. First two
noisy IMFs (i.e., IMF 1 and IMF 2) are removed and the rest IMFs and residue
are used to compress the signal. Then downsampling is applied to these IMFs and
residue. The downsampled signals are summed up and fed as input to the next
stage. Discrete cosine transform (DCT), window filtering and Huffman encoding
techniques are then used sequentially to compress the ECG signal. The reconstruction method consists of Huffman decoding, inverse discrete cosine transform
(IDCT) and spline interpolation. The reproduction (after reconstruction) efficacy
is measured in terms of percent root mean square difference (PRD). Experimental
results show that the proposed technique provides high CR compared to other
techniques. PRD value is also comparable to lossless compression techniques.
• A low cost off-line text messaging service, SMS is used for transmission of compressed ECG. For experimental purpose a PC / laptop with GSM modems was
used to send the ECG signal over wireless link. The SMS messages were sent to a
mobile phone and the received SMS messages were processed for decompression to
reconstruct the ECG signal. Experimental result shows comparable PRD which
can provide better diagnostic information in the reconstructed data.
• A detection algorithm has been proposed to detect heart rhythm abnormalities and
ischemic episodes. The proposed technique first, evaluates the heart rate (HR) and
then uses the predefined normal HR range to identify slow or fast heart rhythm
i.e., bradycardia or tachycardia respectively. In ischemia detection algorithm first,
preprocessing of the signal was performed which involves normalization and filtering. Next feature extraction, beat classification and ischemic episode recognition
are used sequentially to identify ischemic episodes. A health care system is im81
Chapter 5. Conclusion and Future Scope of Work
plemented here. ECG signal is first, compressed and transmitted over wireless
medium. Next, at the receiver the signal is decompressed to reconstruct the original signal. Then the detection technique is carried out to identify various cardiac
disorders. Experimental results show that the proposed technique provides high
sensitivity (Se) and high positive predictive accuracy (PPA) compared to other
techniques. Implementation of this, system can facilitate mobile health care.
5.2
Future Works
In this research work, a remote cardiac patient monitoring method is described to diagnose the cardiac disorders. In future, more works can be implemented for faster and
advanced health care. Some of the future works are described below.
• This work has been done for a single patient communicating with doctor over
wireless network. However, if multiple patient communicate with doctor then
the doctor can not recognize each SMS individually. In future, this work can be
extended to a number of patients communicating with doctor simultaneously with
use of different data frames.
• The work intended in this thesis was is to provide remote health care in a typical
rural area. Here, it is presumed that 2G mobile communication service is available
and there is no internet connectivity. In future, an automatic internet based transmission system can be designed to harness the high speed internet service where
available.
• Currently Windows and Android operating system based smart phones are widely
used. These operating systems facilitate development of applications for different
requirement. In future, tele-cardiology applications for ECG transmission and
detection can be developed. These applications will have potential to provide
coronary health care to remote areas.
82
Chapter 5. Conclusion and Future Scope of Work
• The proposed detection technique can be extended to detect other cardiovascular
disorders like bundle branch block, wolff- parkinson- white syndrome, premature
ventricular contraction, myocardial infraction, etc., with little modification in detection algorithms. Advanced pattern recognition techniques can be applied for
detection of ischemia and other types of cardiac disorders.
• Implementation of a stand-alone low cost hardware system using an embedded
system platform can help to make quality health care affordable to cardiac patients
in remote areas. Once implementation is completed, survey on doctor’s opinion
can be made for the fidelity of the system.
• ECG image compression and reconstruction algorithms can be developed for remote patient monitoring system. Also real time noises can be introduced with
ECG signal to see the robustness of the algorithms. Different filtering techniques
can be used in ECG signal compression and reconstruction algorithms to make the
performance better.
83
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[109] G. K. Sahoo, S. Ari, and S. K. Patra, “ECG signal analysis for detection of Heart
Rate and Ischemic Episodes,” International Journal of Advanced Computer Research, vol. 3, no. 8, pp. 148–152, 2013.
[110] M. Faezipour, T. Tiwari, A. Saeed, M. Nourani, and L. Tamil, “Wavelet-based
denoising and beat detection of ECG signal,” in Proc. Life Science Systems and
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97
PUBLICATIONS
Journal:
[1] G. K. Sahoo, S. Ari, and S. K. Patra, “ECG signal analysis for detection of
Heart Rate and Ischemic Episodes,” International Journal of Advanced Computer
Research, vol. 3, no. 8, pp. 148-152, 2013.
Conference:
[2] G. K. Sahoo, S. Ari, and S. K. Patra, “ECG signal analysis for detection of Cardiovascular abnormalities and Ischemic episodes,” in Proc. IEEE Conf. on Information
& Communication Technologies, 2013, pp. 1055–1059.
[3] B. B. Pradhan, S. Ari, G. K. Sahoo, D. K. Jena, S. K. Patra, and R. Appavuraj,
“Wavelet Transform Based Error Detection in Signal Acquired from Artillery Unit,”
in Proc. 1st Int. Conf. on Condition Assessment Techniques in Electrical Systems,
2013, pp. 243-248.
98
AUTHOR’S BIOGRAPHY
Goutam Kumar Sahoo was born and brought up in Haladiapatana, a small village belongs to Kendrapara district, Odisha. In 1998, he completed his matriculation from
Benipur high school, Benipur, Kendrapara. Then he moved
to Rourkela Muncipal College and in 2000, he passed the
Higher Secondary Examination. He started his engineering
career in 2001 and received B. E. degree in Electronics & Telecommunication Engineering from Biju Patnaik University of Technology (BPUT) Rourkela, in 2005. He worked
for 4 years at Padmanava College of Engineering, Rourkela as a faculty member. He
joined as a Junior Research Fellow (JRF) and got admission to M. Tech. (Research)
programme at National Institute of Technology (NIT) Rourkela, Odisha. He worked for
15 months as JRF in a Defence Research & Development Organisation (DRDO) sponsored project at NIT Rourkela. After completion of sponsored project work, he rejoined
Padmanava College of Engineering and continued his research work from there. He is
currently pursuing the M. Tech. (Research) at NIT Rourkela. His research area is pattern recognition application, biomedical signal processing and telemedicine applications.
He is a life member of ISTE and also a student member of IEEE. He can be contacted
at: [email protected]
99
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