SRECG: ECG Signal Super-resolution Framework for Portable/Wearable Devices in Cardiac Arrhythmias Classification
This work is significant for patients requiring long-term cardiac monitoring, as it enables the use of low-resolution ECG signals from portable devices without significant loss in diagnostic accuracy, thus extending battery life and reducing bandwidth usage.
This paper proposes SRECG, a deep learning-based framework to enhance low-resolution ECG signals from portable/wearable devices. It improves the accuracy of a high-resolution multiclass classifier (HMC) for cardiac arrhythmias, maintaining approximately half of the HMC classification accuracies. The method addresses the challenge of low sampling rates due to battery and bandwidth limitations in continuous ECG monitoring.
A combination of cloud-based deep learning (DL) algorithms with portable/wearable (P/W) devices has been developed 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 electronics (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 signals 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 interpolation methods. Moreover, approximately half of the CAs classification accuracies of HMC were maintained within the enhanced 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.