Published Paper Details:

Electrocardiogram (ECG), a non-invasive 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 peak classification approach is used in ECG signal for determining various diseases. As known the amplitudes and duration values of P-Q-R-S-T peaks determine the functioning of heart of human. Therefore, duration and amplitude of all peaks are found. R-R and P-R intervals are calculated. Finally, we have obtained the necessary information for disease detection. For detection of cardiac arrhythmias; the extracted features in the ECG signal will be input to the classifier. The extracted features contain morphological l features of each heartbeat in the ECG signal. We have detected bradycardia and tachycardia. Massachusetts Institute of Technology Beth Israel Hospital (MIT-BIH) arrhythmias database has been used for performance analysis.