ECG WAVE COMPONENT DETECTION & CLASSIFICATION

Abstract

Electrocardiogram (ECG) is a graphical representation of the electromechanical activity of the cardiac system. It provides fast and reliable information to the expert cardiologist with respect to the functional aspects of the heart. The ECG is recorded in many: situations, viz., to know the state of a patient under medical diagnosis and treatment, to keep a watch on the state of patients in the intensive cardiac care units, to know the response of the patient under medical treatment, to know the condition of cardiac system under stressed conditions, and to monitor the state of the ambulatory patients. The number of cardiac patients are increasing at an alarming rate and it is not possible for the existing number of cardiologists to take care of all the cardiac patients under all the conditions. This problem was realized about four decades back and a large number of individuals and groups started work on the computer aided analysis and interpretation of ECG signal throughout the world. The ECG data acquisition and preprocessing: detection of waves, peaks, feature extraction of all waves, peaks; and usage of these in disease classification and diagnostics are the important stages in computer aided ECG analysis and interpretation. The work has been carried out in this thesis for automated analysis and interpretation of the ECG signal by making effective use of the wavelet transform and principal component analysis. The work covers the ECG feature detection & extraction, and cardiac disease diagnostics using multilead ECGs. With the aim of improving extraction of parameters with 100% accuracy, ECG parameters are extracted with the help of wavelet transforms and also with the combination of wavelet transforms and principal component analysis and it is found that results are better after applying Principal Component Analysis. The developed algorithm works satisfactorily in all typical morphologies of the ECG. The developed algorithm gives the QRS detection rate of 99.33% with the sensitivity of 100% when tested on records of CSE database. Here I have taken 15 records on which testing has been done. The ultimate aim of ECG analysis is disease classification. Here I have taken 10 records of CSE database on which disease classification has been made.