Key result
The SRECG deep learning framework enhanced low-resolution ECG signals, improving cardiac arrhythmia classification F1-scores compared to traditional interpolation methods and recovering up to 58% of predictive power.
Why the study?
Decreasing ECG sampling rates to address battery and bandwidth limits in portable devices produces low-resolution signals that impair cardiac arrhythmia classification.
Does a deep learning-based ECG signal super-resolution framework improve cardiac arrhythmia classification accuracy from low-resolution ECG signals?
Does a deep learning-based ECG signal super-resolution framework improve cardiac arrhythmia classification accuracy from low-resolution ECG signals?
A novel deep learning-based super-resolution framework can enhance low-resolution ECG signals from wearable devices to improve automated arrhythmia classification accuracy.
May facilitate arrhythmia detection from wearable ECGs; leaves open prospective clinical validation and outcome impact.
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.
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Chen et al. (2020) studied Cardiac arrhythmias (n=6,401). SRECG (Deep learning-based ECG signal super-resolution framework) vs. Traditional spline interpolation methods and unenhanced low-resolution signals was evaluated on F1-score for cardiac arrhythmia classification. The SRECG deep learning framework enhanced low-resolution ECG signals, improving cardiac arrhythmia classification F1-scores compared to traditional interpolation methods and recovering up to 58% of predictive power.
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