Key result
Open-access 24-hour wearable ECG database enables evaluation of algorithms detecting PVCs and SPBs.
Why the study?
Existing premature beat detection methods struggle with poor signal quality from dry electrodes in dynamic wearable ECG systems.
This open-access database provides a standardized resource for developing and evaluating algorithms to detect premature beats in long-term wearable ECG recordings.
May accelerate PVC/SPB algorithm development for wearables; leaves open prospective outcome validation before clinical use.
Wearable electrocardiogram (ECG) devices can provide real-time, long-term, non-invasive and comfortable ECG monitoring for premature beats (PB) assessment (typically presenting as premature ventricular contractions (PVC) and supraventricular premature beat (SPB)), which may foreshadow stroke or sudden cardiac death. However, the poor quality, introduced by the dry electrode in wearable ECG monitoring system, leads to the inefficient recognition of the existing PB detection technologies. Although many methods can achieve high recognition rate on current widely-used open-access clinical ECG databases, they still fail to work properly on dynamic ECG signals. This study presents an open-access ECG database comprises of 24-hour wearable ECG recordings. The database is used for the 3rd China Physiological Signal Challenge (CPSC 2020), where participants are expected to recognize PVC and SPB from these recordings. All the approved algorithms are evaluated by scoring standards and regulations defined in terms of PVC detection and SPB detection, respectively.
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Cai et al. (2020) studied Premature ventricular contractions and supraventricular premature beats. Open-access wearable ECG database was evaluated on Recognition of premature ventricular contractions and supraventricular premature beats. An open-access database comprising 24-hour wearable ECG recordings was developed for the 3rd China Physiological Signal Challenge to evaluate algorithms for detecting PVCs and SPBs.
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