Abstract: Electrocardiogram (ECG) is the time varying signal represents the heart’s electrical activity. The signal provides the information of coronary heart diseases, rhythm disorders etc. The classification of ECG signal is difficult since the morphological and temporal characteristics of ECG signal shows significant variations for different patients under different physical conditions. Many techniques available for analysis such as Neuro-Fuzzy, Self-organizing maps etc,. This uses time-plane features ST, R, T segments thus increasing the computational complexity. In this paper, we use cross wavelet transform for the analysis and classification of electrocardiogram (ECG) signals. The cross-correlation between two time varying signals gives a measure of similarity between two waveforms. The application of the continuous wavelet transform to two time varying signals and the cross examination of the two decompositions reveal localized similarities in time and frequency. The application of the cross wavelet transform to signal yields wavelet cross spectrum (WCS) and wavelet coherence (WCOH). The proposed algorithm analyzes ECG data utilizing cross wavelet transform and explores the resulting spectral differences. A pathologically varying pattern from the normal pattern in the QT zone of the inferior leads shows the presence of inferior myocardial infarction. A normal beat ensemble is selected as the absolute normal ECG pattern template, and the coherence between various other normal and abnormal subjects is computed. The WCS and WCOH 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. The Physikalisch-Technische Bundesanstalt diagnostic ECG database is used for evaluation of the methods. A heuristically determined mathematical formula extracts the parameter from the WCS and WCOH. Empirical tests establish that the parameters are relevant for classification of normal and abnormal cardiac patterns. The overall accuracy, sensitivity and specificity after combining the three leads are obtained.
Keywords: Cross wavelet transform, fiducial points, interpolation, myocardial infarction, wavelet coherence (WCOH).
Title: Similarity Measurement Technique of Two ECG Signals Analysis and Pattern Classification
Author: Anil Kumar S. N, Dr. Sarika Tale
International Journal of Electrical and Electronics Research
ISSN 2348-6988 (online)
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