CS-based multi-task learning network for arrhythmia reconstruction and classification using ECG signals

Abstract

Although deep learning-based current methods have achieved impressive results in electrocardiograph (ECG) arrhythmia classification issues, they rely on using the original data to identify arrhythmia categories. However, a large amount of data generated by long-term ECG monitoring pose a significant challenge to the limited-bandwidth and real-time systems, which limits the application of deep learning in ECG monitoring. This paper, therefore, proposed a novel multi-task network that combined compressed sensing (CS) and convolutional neural networks, namely CSML-Net. According to the proposed model, the ECG signals were compressed by utilizing a learning measurement matrix and then recovered and classified simultaneously via shared layers and two task branches. Among them, the multi-scale feature module was designed to improve model performance. Experimental results on the MIT-BIH arrhythmia dataset demonstrate that our proposed method is superior to all the approaches that have been compared in terms of reconstruction quality and classification performance. Consequently, the proposed model achieving the reconstruction and classification in the compressed domain can be an improvement and become a promising approach for ECG arrhythmia reconstruction and classification.