An approach for ECG based cardiac abnormality detection through the scope of Cross Wavelet Transform

Abstract

The analysis of standard clinical electrocardiogram signal is one of the basic routine tests for preliminary screening of cardiac abnormalities. This work deals with classification of normal and IMI (Inferior Myocardial Infaction) and presents a method for analysis of ECG patterns using Cross Wavelet Transform (XWT). The cross-correlation between two time domain signals gives the measure of similarity between two waveforms. The application of the Continuous Wavelet Transform to two time series and the cross examination of the two decomposition reveals localized similarities in time and scale. A pathologically varying pattern in QT zone of inferior lead III, shows the presence of Inferior Myocardial Infarction (IMI). Application of Cross Wavelet Transform to a pair of data gives wavelet cross spectrum and wavelet coherence. A normal beat template is selected as the absolute normal ECG pattern and the coherence between various other normal and abnormal subjects is computed. The Wavelet cross spectrum and Wavelet coherence of various ECG patterns show distinguishing characteristics over two specific regions R1 and R2, where R1 is the QRS complex area and R2 is the T wave region. PTB diagnostic ECG database is used for evaluation of the methods. A heuristically determined mathematical formula extracts parameter(s) from the wavelet cross spectrum and coherence. Empirical tests establish that the parameter is relevant for classification of normal and abnormal Cardiac patterns. The classification accuracy is obtained as 92.5% respectively.