Automated screening of arrhythmia using wavelet based machine learning techniques

Arrhythmia is one of the preventive cardiac problems frequently occurs all over the globe. In order to screen such disease at early stage, this work attempts to develop a system approach based on registration, feature extraction using discrete wavelet transform (DWT), feature validation and classification of electrocardiogram (ECG). This diagnostic issue is set as a two-class pattern classification problem (normal sinus rhythm versus arrhythmia) where MIT-BIH database is considered for training, testing and clinical validation. Here DWT is applied to extract multi-resolution coefficients followed by registration using Pan Tompkins algorithm based R point detection. Moreover, feature space is compressed using sub-band principal component analysis (PCA) and statistically validated using independent sample t-test. Thereafter, the machine learning algorithms viz., Gaussian mixture model (GMM), error back propagation neural network (EBPNN) and support vector machine (SVM) are employed for pattern classification. Results are studied and compared. It is observed that both supervised classifiers EBPNN and SVM lead to higher (93.41% and 95.60% respectively) accuracy in comparison with GMM (87.36%) for arrhythmia screening.