An intelligent medical decision support system for diagnosis of heart abnormalities in ECG signals

This work describes a novel method to detect a Bundle branch block and myocardial infarction from the multi-lead ECG signal. The clinical characteristics of BBB and MI extracted by using a derivative filter and continuous wavelet transform (CWT). The signal with the frequency below 50 Hz obtained and derivative-based filter applied to extract features. The continuous wavelet transforms also applied to the signals of BBB and MI. The CWT coefficients extracted, and the signals reconstructed from the wavelet to obtain the features. The feature vectors generated from each lead of both the methods computed using parameters such as spectral entropy, mean of peaks, total energy from power spectrum density, form factor, and root mean squared value. The results of both the derivative-based filter and CWT analyzed by applying these features to the classifiers. The accuracy of classification of diseases computed using SVM, KNN, Levenberg-Marquardt Neural Network (LMNN), and scaled conjugate gradient backpropagation network (SCG NN). The best accuracy obtained from the derivative filter and wavelet transform method is 96.4% using LMNN and SCGNN classifier and 96.4% using KNN and LMNN classifier respectively.