DeepArrNet: An Efficient Deep CNN Architecture for Automatic Arrhythmia Detection and Classification From Denoised ECG Beats

Abstract

In this paper, an efficient deep convolutional neural network (CNN) architecture is proposed based on depthwise temporal convolution along with a robust end-to-end scheme to automatically detect and classify arrhythmia from denoised electrocardiogram (ECG) signal, which is termed as `DeepArrNet'. Firstly, considering the variational pattern of wavelet denoised ECG data, a realistic augmentation scheme is designed that offers a reduction in class imbalance as well as increased data variations. A structural unit, namely PTP (Pontwise-Temporal-Pointwise Convolution) unit, is designed with its variants where depthwise temporal convolutions with varying kernel sizes are incorporated along with prior and post pointwise convolution. Afterward, a deep neural network architecture is constructed based on the proposed structural unit where series of such structural units are stacked together while increasing the kernel sizes for depthwise temporal convolutions in successive units along with the residual linkage between units through feature addition. Moreover, multiple depthwise temporal convolutions are introduced with varying kernel sizes in each structural unit to make the process more efficient while strided convolutions are utilized in the residual linkage between subsequent units to compensate the increased computational complexity. This architecture provides the opportunity to explore the temporal features in between convolutional layers more optimally from different perspectives utilizing diversified temporal kernels. Extensive experimentations are carried out on two publicly available datasets to validate the proposed scheme that results in outstanding performances in all traditional evaluation metrics outperforming other state-of-the-art approaches.