Diagnostic validation of smart wearable device embedded with single-lead electrocardiogram for arrhythmia detection

Abstract

Objective To validate a single-lead electrocardiogram algorithm for identifying atrial fibrillation, atrial premature beats, ventricular premature beats, and sinus rhythm. Methods A total of 656 subjects aged 19 to 94 years were enrolled. Participants were simultaneously tested with a wristwatch (Huawei Watch GT2 Pro, Huawei Technologies Co., Ltd, Shenzhen, China) and a 12-lead electrocardiogram for 3 minutes. A total of 1926 electrocardiogram signals from 628 subjects (282 men and 346 women) aged 19 to 94 years (median 64 years) were analyzed using an algorithm. Results The numbers of subjects with atrial fibrillation, atrial premature beats, ventricular premature beats, and sinus rhythm were 129, 141, 107, and 251, respectively, and together they had a total of 1926 electrocardiogram signals. For the three-class classification system, the recall, precision, and F1 score were 97.6%, 96.5%, 97.0% for sinus rhythm; 96.7%, 96.9%, 96.8% for atrial fibrillation; and 92.8%, 94.2%, 93.5% for ectopic beats, respectively. The macro-F1 score of the three-class classification system was 95.8%. For the four-class classification system, the recall, precision, and F1 score were 97.6%, 96.5%, 97.0% for sinus rhythm; 96.7%, 96.9%, 96.8% for atrial fibrillation; 90.5%, 89.4%, 89.9% for atrial premature beats; and 86.1%, 89.6%, 87.8% for ventricular premature beats, respectively. The macro-F1 score of the four-class classification system was 92.9%. Conclusions The single-lead electrocardiogram algorithm embedded into smart wearables demonstrated good performance in detecting atrial fibrillation, atrial/ventricular premature beats, and sinus rhythm, and thus would facilitate atrial fibrillation screening and management.

The robot butler: How and why should we study predictive algorithms and artificial intelligence (AI) in healthcare?

“… 4 , 5 Yet while bioinformatics research projects proliferate, integrating huge amounts and kinds of health data, the resulting algorithms seldom reach clinical implementation and use 6 – for illustrative examples in the field of cardiology, see recently published studies in this journal. 7 – 12 In other areas (e.g., social media, the banking community, insurance and advertising), AI tools have been easier to bring to use, and in response, social science scholars have demonstrated the social, ethical and organizational implications of algorithmic systems for automating and supporting decision-making. 13 – 19 Of particular scholarly concern are issues of algorithmic bias and discrimination, the delegation of agency and authority from humans to AI and the complexity and lack of transparency in algorithmic systems that render difficult or even impossible to assess accountability in complex human-algorithm relations and processes.…”

Artificial intelligence-enabled atrial fibrillation detection using smartwatches: current status and future perspectives

“…Moreover, the PPG signal may be weaker in elderly, due to pathologic or physiologic changes associated with aging including reduced peripheral blood flow, increased arterial wall stiffness, and skin changes ( 35 , 36 ). On the other hand, there may be a low amplitude of P-waves with single-lead ECG, which challenges the classification of electrical activity ( 37 ). Lastly, limitations of using smartwatch to detect AF include the necessity for user cooperation and regular charging, depending on its battery life ( 10 ).…”