An End-to-End Multi-Level Wavelet Convolutional Neural Networks for heart diseases diagnosis

Abstract

This paper presents a new End-to-End Deep Learning method for heart diseases diagnosis from single channel ECG signal. Motivated by the great efficiency and popularity of deep learning algorithms for time series classification, the proposed work is mainly based on the One Dimensional Convolutional Neural Networks (1D-CNN). Unlike the traditional CNN models based classification, a new Multi-Level Wavelet Convolutional Neural Networks (ML-WCNN) is proposed to recognize automatically various types of cardiac arrhythmias. The proposed approach incorporates the 1D-CNN model and the Stationary Wavelet Transform (SWT) to extract discriminative features from different wavelet sub-bands and from the raw ECG signal simultaneously. The extracted features by the ML-WCNN model are then merged using different fusion strategies, especially by concatenation and maximization. This improves greatly the features learning process at different scales of the ECG signal, providing better diagnosis performances. The proposed ML-WCNN framework is evaluated on the Standard Database MIT-BIH Arrhythmia considering six classes of heart Beats: Normal (N), Premature Ventricular Contraction (PVC), Right Bundle Brunch Block (RBBB), Left Bundle Brunch Block (LBBB), Atrial Premature Contraction (APC) and Paced beats (PAC). The experimental results demonstrate the superiority of the proposed ML-WCNN, in comparison with the state-of-the-art heart diseases diagnosis based Machine/Deep learning algorithms, with a maximum Accuracy of 99.57% using the 10-fold cross validation technique.