Detection of cardiac abnormality using discrete wavelet coefficients of ECG

Abstract

The electrocardiogram (ECG) is the record of variation of bioelectric potential of human heart beats with respect to time. ECG represents the electrical activity of heart and is a well-established tool for the diagnosis of cardiac condition. In order to obtain accurate information about cardiac condition of a person, correct interpretation of ECG is essential. The objective of this work is to devise an automatic computer based detection technique for five particular types of cardiac abnormalities by processing ECG signal with better accuracy and less computational time. Five particular types of cardiac phenomena that have been considered are: normal beat, left bundle branch block (LBBB), right bundle branch block (RBBB), premature ventricular contraction (PVC) and atrial premature beat (APB). A total set of 19 records have been taken from MIT- BIH arrhythmia database as test data. For all the test records, discrete wavelet transform (DWT) with 40,000 samples up to a decomposition level of 4 is performed in the Matlab 7.4.0 environment. Five different types of mother wavelet functions are used for this analysis. These are: haar, coiflet2, symlet4, daubechies10 and biorthogonal6.8. The maximum value of the approximation coefficients of level 4 is selected as the indicating parameter, which is used to distinguish between different abnormalities. A comparison is made among the performances of different types of mother wavelets to select the best one to differentiate cardiac abnormalities. Among the five wavelets that we have used in our study, daubechies10 (db10), the only one possessing asymmetric property, has provided the most optimum result. So as an outcome of this analysis we can conclude that asymmetric mother wavelet functions are more effective than the symmetric ones for distinguishing different types of ECG beats. Further analysis with more number of symmetric and asymmetric wavelet functions should be carried out to generalize the findings of this study and to detect other types of cardiac abnormalities.