  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.
