Analysis of ECG signal for Detection of Cardiac Arrhythmias

Abstract

Electrocardiogram (ECG), a noninvasive technique is used as a primary diagnostic tool for cardiovascular diseases.
A cleaned ECG signal provides necessary information about the electrophysiology of the heart diseases and
ischemic changes that may occur. It provides valuable information about the functional aspects of the heart and
cardiovascular system. The objective of the thesis is to automatic detection of cardiac arrhythmias in ECG signal.
Recently developed digital signal processing and pattern reorganization technique is used in this thesis for detection
of cardiac arrhythmias. The detection of cardiac arrhythmias in the ECG signal consists of following stages:
detection of QRS complex in ECG signal; feature extraction from detected QRS complexes; classification of beats
using extracted feature set from QRS complexes. In turn automatic classification of heartbeats represents the
automatic detection of cardiac arrhythmias in ECG signal. Hence, in this thesis, we developed the automatic
algorithms for classification of heartbeats to detect cardiac arrhythmias in ECG signal.
QRS complex detection is the first step towards automatic detection of cardiac arrhythmias in ECG signal. A novel
algorithm for accurate detection of QRS complex in ECG signal is proposed in chapter 2 of this thesis. The detection
of QRS complex from continuous ECG signal is computed using autocorrelation and Hilbert transform based
technique. The first differential of the ECG signal and its Hilbert transformed is used to locate the R-peaks in the
ECG waveform. The autocorrelation based method is used to find out the period of one cardiac cycle in ECG signal.
The advantage of proposed method is to minimize the large peak of P-wave and T-wave, which helps to identify the
R-peaks more accurately. Massachusetts Institute of Technology Beth Israel Hospital (MIT-BIH) arrhythmias
database has been used for performance analysis. The experimental result shows that the proposed method shows
better performance as compared to the other two established techniques like Pan-Tompkins (PT) method and the
technique which uses the difference operation method (DOM).
For detection of cardiac arrhythmias, the extracted features in the ECG signal will be input to the classifier. The
extracted features contain both morphological and temporal features of each heartbeat in the ECG signal. Twenty six
dimension feature vector is extracted for each heartbeat in the ECG signal which consist of four temporal features,
three heartbeat interval features, ten QRS morphology features and nine T-wave morphology features.
Automatic classification of cardiac arrhythmias is necessary for clinical diagnosis of heart disease. Many researchers
recommended Association for the Advancement of Medical Instrumentation (AAMI) standard for automatic
classification of heartbeats into following five beats: normal beat (N), supraventricular ectopic beat (S), ventricular
ectopic beat (V), fusion beat (F) and unknown beat (Q). The beat classifier system is adopted in this thesis by first
training a local-classifier using the annotated beats and combines this with the global-classifier to produce an
adopted classification system. The Multilayer perceptron back propagation (MLP-BP) neural network and radial
basis function (RBF) neural network are used to classify the cardiac arrhythmias. Several experiments are performed
on the test dataset and it is observed that MLP-BP neural network classifies ECG beats better as compared to RBF
neural network.