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

• An efficient algorithm for ECG heartbeats classification is proposed. The proposed algorithm termed ML-WCNN is based on Convolutional Neural Networks from the deep learning theory, and the Stationary Wavelet Transform (SWT). • The proposed End-to-End deep learning architectures can automatically learn effective discriminative features from the original ECG signal and its wavelets coefficients simultaneously. • The developed ML-WCNN methods can recognize effectively six ECG beat classes without requirement of any pre-processing or features extraction steps. • ECG signals form the standard database MIT-BIH Arrhythmia have been used for evaluating the proposed approach in comparison with others established Machine and Deep learning techniques. • The quantitative results show that the proposed method exhibits a higher or comparable performances for ECG classification on the against some state of the art algorithms with a maximum accuracy of 99.57%. 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. [ABSTRACT FROM AUTHOR]