ECG Signal Super-resolution by Considering Reconstruction and Cardiac Arrhythmias Classification Loss

• URL: http://arxiv.org/abs/2012.03803v1
• Date: Mon, 7 Dec 2020 15:43:50 GMT
• Title: ECG Signal Super-resolution by Considering Reconstruction and Cardiac Arrhythmias Classification Loss
• Authors: Tsai-Min Chen (1 and 2), Yuan-Hong Tsai (3 and 4), Huan-Hsin Tseng (2), Jhih-Yu Chen (5), Chih-Han Huang (6), Guo-Yuan Li (3 and 4), Chun-Yen Shen (1 and 7) and Yu Tsao (1 and 2) ((1) Graduate Program of Data Science, National Taiwan University and Academia Sinica, Taipei, Taiwan, (2) Research Center for Information Technology Innovation, Academia Sinica, Taipei, Taiwan, (3) Taiwan AI Academy, Science and Technology Ecosystem Development Foundation, Taipei, Taiwan, (4) Artificial Intelligence Foundation, Taipei, Taiwan, (5) Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan, (6) Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan, (7) Department of Mathematics, National Taiwan University, Taipei, Taiwan)
• Abstract summary: We propose a deep-learning-based ECG signal super-resolution framework (termed ESRNet) to recover compressed ECG signals. Experimental results show that the proposed ESRNet framework can well reconstruct ECG signals from the 10-times compressed ones.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: With recent advances in deep learning algorithms, computer-assisted healthcare services have rapidly grown, especially for those that combine with mobile devices. Such a combination enables wearable and portable services for continuous measurements and facilitates real-time disease alarm based on physiological signals, e.g., cardiac arrhythmias (CAs) from electrocardiography (ECG). However, long-term and continuous monitoring confronts challenges arising from limitations of batteries, and the transmission bandwidth of devices. Therefore, identifying an effective way to improve ECG data transmission and storage efficiency has become an emerging topic. In this study, we proposed a deep-learning-based ECG signal super-resolution framework (termed ESRNet) to recover compressed ECG signals by considering the joint effect of signal reconstruction and CA classification accuracies. In our experiments, we downsampled the ECG signals from the CPSC 2018 dataset and subsequently evaluated the super-resolution performance by both reconstruction errors and classification accuracies. Experimental results showed that the proposed ESRNet framework can well reconstruct ECG signals from the 10-times compressed ones. Moreover, approximately half of the CA recognition accuracies were maintained within the ECG signals recovered by the ESRNet. The promising results confirm that the proposed ESRNet framework can be suitably used as a front-end process to reconstruct compressed ECG signals in real-world CA recognition scenarios.

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