ECG Heartbeat Classification Based on Multi-Scale Wavelet Convolutional Neural Networks

Abstract

This paper proposes a novel Deep Learning technique for ECG beats classification. Unlike the traditional Deep Learning models, a new Multi-Scale Wavelet Convolutional Neural Networks (MS-WCNN) is proposed to recognize automatically various cardiac arrhythmias. The proposed MS-WCNN model incorporates the one dimensional CNN and the Stationary Wavelet Transform (SWT) to extract discriminative features from the ECG signal and its wavelet sub-bands simultaneously. The extracted features are then merged using a concatenation strategy. This improves greatly the features learning process of our model at different scales, providing better diagnosis performances. The MITBIH Arrhythmia database has been used to evaluate the performance of the developed model, considering five heartbeats classes: Non-ectopic beat, Supra ventricular ectopic beat, Ventricular ectopic beat, Fusion beat and Unknown beat. The obtained results show that the MS-WCNN method achieves higher or comparable performances with respect to the existing ECG classification algorithms, with an overall diagnosis accuracy of 99, 11%.