Application of crosswavelet transform and Wavelet Coherence for classification of ECG patterns

This paper presents a method for classification of ECG patterns using Cross Wavelet Transform (XWT) and Wavelet Coherence (WC) techniques. The cross-correlation is 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). In this work classification of normal and IMI is studied. The Cross Wavelet Transform and Wavelet Coherence is used for the cross examination of a single normal and abnormal (IMI) beats. A normal beat template is selected as the absolute normal pattern and the coherence between various other normal and abnormal is computed. A parameter pa, equal to the summation of the coherence values over the QT zone distinguishes normal and abnormal clusters. From this cluster a threshold value is determined, which is used for classification of the subjects. All data for the purpose of analysis is considered from PTB diagnostic ECG database. The classification accuracy and sensitivity is obtained as 90% respectively.