A lightweight 2-D CNN model with dual attention mechanism for heartbeat classification

Hongfu Xie, Hui Liu, Shuwang Zhou, Tianlei Gao, Minglei Shu

Abstract: The classification of heartbeats is important for the diagnosis of arrhythmia, and more and more attention has been paid to deep learning methods to avoid manual feature design and extraction. Many methods are proposed based on the two-dimensional image representation of heartbeat and achieve good results, but these methods usually have a large number of parameters and high computational cost, which are not conducive to the real-world applications (e.g. wearable health monitoring). To address the problem, we propose a lightweight and high-performance inter-patient heartbeat classification method with dual attention mechanism. Specifically, we design a lightweight residual block, which is mainly composed of residual and depthwise separable convolution, for reducing the number of parameters. The dual attention mechanism is implemented by combining the “Squeeze-and-Excitation” (SE) module and a new “Convolutional Squeeze-and-Excitation” (Conv-SE) module. Two different kinds of channel attention are fused to improve the performance of the model. In addition, an effective image-based data augmentation procedure for 2-D representation of heartbeats is developed to overcome the data imbalance problem. We evaluated the proposed method on the MIT-BIH Arrhythmia Database and the Supraventricular Arrhythmia Database. Our method achieved an average classification accuracy of 98.68% and an average F1-score of 0.9202 on the MIT-BIH Arrhythmia database, and achieved an average classification accuracy of 97.46% and an average F1-score of 0.8984 on the Supraventricular Arrhythmia Database. Compared with other methods, the proposed method achieved best classification performance, and the number of parameters and the computational complexity were reduced by more than 70%.