SRECG: ECG Signal Super-Resolution Framework for Portable/Wearable Devices in Cardiac Arrhythmias Classification
Tsai‐Min Chen, Yuan-Hong Tsai, H. Eric Tseng, Kai-Chun Liu, Jhih-Yu Chen, Chih-Han Huang, Guoyuan Li, Chun‐Yen Shen, Yu Tsao
Abstract
A combination of cloud-based deep learning (DL) al-gorithms with portable/wearable (P/W) devices has been devel-oped as a smart heath care system to support automatic cardiac arrhythmias (CAs) classification using electrocardiography (ECG). However, long-term and continuous ECG monitoring is challenging because of limitations of batteries and transmission bandwidth of P/W devices while incorporated with consumer elec-tronics (CE). A feasible approach to address this challenge is to decrease sampling rates. However, low sampling rates lead to low-resolution signals that hinder the CAs classification performance. In this study, we propose a DL-based ECG signal super-resolution framework (called SRECG) to enhance low-resolution ECG sig-nals by jointly considering the accuracies when applied to the DL-based high-resolution multiclass classifier (HMC) of CAs. In our experiments, we downsampled the ECG signals from the CPSC2018 dataset and evaluated their HMC accuracies with and without the SRECG. Experimental results show that SRECG can well improve the HMC accuracies as compared to traditional in-terpolation methods. Moreover, approximately half of the CAs classification accuracies of HMC were maintained within the en-hanced ECG signals by SRECG. The promising results confirm that SRECG can be suitably used to enhance low-resolution ECG signals from P/W devices with CE to improve their cloud-based HMC performances.