Abstract:
Electrocardiogram (ECG), is a graphic recording of the electrical activity produced by the heart. The accuracy of any Electrocardiogram (ECG) waveform extraction plays a vital role in helping a better diagnosis on any heart related illnesses. A cleaned ECG signal provides necessary information about the electrophysiology of the heart diseases and ischemic changes that may occur. The objective of the thesis is to developed Computer-aided application to analyse electrocardiograph (ECG) signals for detection of cardiac arrhythmias. The detection of cardiac arrhythmias in the ECG signal consists of the following stages: signal pre-processing, detection of the ECG signal components, feature extraction from the detected QRS complexes, and classification of the beats extracted from QRS complexes using Back Propagation Neural Network (BPNN). In turn automatic classification of heartbeats represents the automatic detection of cardiac arrhythmias in the ECG signal. In this thesis, the algorithms and graphical user interface (GUI) were developed for ECG signal classification under ‘Normal’ or ‘Abnormal’ heartbeats to detect cardiac arrhythmias in the ECG signal. The application was developed and trained with standard arrhythmia database of Massachusetts Institute of Technology Division of Health Science and Technology’s (MIT/BIH) and taking into account the Association for the Advance of Medical Instrumentation (AAMI) standard. The performance of the developed application for classification of ECG signals was investigated using the MIT-BIH database. The results obtained in terms of classification accuracy of over 71% (based on MIT/BIH database used) shows that the proposed system can be employed for the detection of heart diseases in patients.
