An open-access database was constructed containing 2000 30-second wearable ECG recordings of sinus, atrial, and ventricular arrhythmias from over 200 patients to support algorithm development.
The construction of an open-access, annotated wearable ECG database containing 2,000 recordings provides a valuable resource for developing and validating automated arrhythmia detection algorithms.
Abstract Purpose Heart arrhythmias result from any disturbance in the rate, regularity, and site of origin or conduction of the cardiac electric pulse. Sporadic and underappreciated characteristics make diagnosis less timely, leading to stroke, heart failure, or even sudden death. Wearable electrocardiogram (ECG) devices are gradually becoming the main trend of intelligent diagnosis alongside the improvement of information transmission and computation power of the hardware. Therefore, a database for arrhythmia detection was planned to construct. Methods Collaborating with hospital, this study presents a bipolar limb two-lead wearable device by collecting a large amount of real-time data and giving rise to readily available ECG databases. Results In total, the database contains 2000 30-s recordings of sinus, atrial and ventricular arrhythmias collected from more than 200 voluntary patients who had been diagnosed with heart diseases, ranging in age from 18 to 82. Meanwhile, manual annotations by cardiologists were proposed to benefit and instruct non-medical researchers to design the algorithm reasonably.
Shen et al. (Wed,) conducted a other in Arrhythmias (n=200). Wearable wireless ECG monitor was evaluated on Construction of an annotated ECG database. An open-access database was constructed containing 2000 30-second wearable ECG recordings of sinus, atrial, and ventricular arrhythmias from over 200 patients to support algorithm development.
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