Automated detection of myocardial infarction in ECG using modified Stockwell transform and phase distribution pattern from time-frequency analysis

Abstract

Myocardial infarction (MI), usually referred as heart attack, takes place when blood circulation stops to specific portion of the heart resulting permanent damage to the heart muscles. It is an important task to identify the occurrence of MI from the ECG recordings efficiently. Most of the detection procedures include advanced signal processing methods, more ECG features and composite classifiers, making the overall procedure complex. This paper aims at automated identification of MI using modified Stockwell transform (MST) based time-frequency analysis and a phase information distribution pattern method. The morphological, pathological and temporal alterations in ECG waveforms resulting from the onset of MI are noticed in the phase distribution pattern of the ECG signal. Two discriminating features, utterly reflecting these alterations, are recognized for 12 leads of the MI affected ECG signal. Prior informations regarding the pathological characteristics of the specific disease are required for the correct detection of MI using few numbers of ECG leads. Thus, in this paper 12 lead ECG signals have been considered for identification of MI. The two-class classification problem with MI class and healthy individual class is performed using the threshold based classification regulation. Both healthy control and MI affected ECG signals are collected from the PTB diagnostic ECG database. The accuracy, sensitivity and specificity are found to be 99.93%, 99.97% and 99.30% for detection of MI. The proposed method has got the superiority in terms of simplicity of features, small feature dimension and simpler classification rule ensuring faster, accurate and easier MI detection.