Wavelet-based arrhythmia detection of ECG signal and performance measurement using diverse classifiers

Abstract

The diagnosis of cardiovascular arrhythmias needs accurate predictive models to test abnormalities in the functioning of the heart. The proposed work manifests a comparative analysis of different classifiers like K-nearest neighbour (KNN), support vector machine (SVM), back propagation neural network (BPNN), feed forward neural network (FFNN) and radial basis function neural network (RBFNN) with discrete wavelet transform (DWT) to assess an Electrocardiogram (ECG). For DWT, different wavelets such as Daubechies, Haar, Symlet, Biorthogonal, reverse Biorthogonal and Coiflet are used for feature extraction and their performances are compared. SVM and RBFNN have shown 100% accuracy with reduced dataset testing time of 0.0025 s and 0.0174 s, respectively, whereas BPNN, FFNN and KNN provided 95.5%, 97.7% and 84.0% accuracy with 0.0176 s, 0.0189 s and 0.0033 s of testing time, respectively. This proposed scheme builds an efficient selection of wavelet with best-suited classifier for timely perusal of cardiac disturbances.