Niu Yonghong, Wang Hao, Wang Hong, Zhang Hui, Jin Zhigeng, Guo Yutao
Department of Cardiology, The First Affiliated Hospital of Tsinghua University, Beijing, China.
Department of Cardiology, Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China.
Digit Health. 2023 Aug 30;9:20552076231198682. doi: 10.1177/20552076231198682. eCollection 2023 Jan-Dec.
To validate a single-lead electrocardiogram algorithm for identifying atrial fibrillation, atrial premature beats, ventricular premature beats, and sinus rhythm.
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.
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%.
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.
验证一种用于识别心房颤动、房性早搏、室性早搏和窦性心律的单导联心电图算法。
共纳入656名年龄在19至94岁之间的受试者。参与者同时使用一款手表（华为Watch GT2 Pro，华为技术有限公司，中国深圳）和12导联心电图进行3分钟测试。使用一种算法对来自628名年龄在19至94岁（中位数64岁）的受试者（282名男性和346名女性）的1926份心电图信号进行了分析。
患有心房颤动、房性早搏、室性早搏和窦性心律的受试者人数分别为129、141、107和251，他们总共拥有1926份心电图信号。对于三级分类系统，窦性心律的召回率、精确率和F1分数分别为97.6%、96.5%、97.0%；心房颤动为96.7%、96.9%、96.8%；异位搏动为92.8%、94.2%、93.5%。三级分类系统的宏F1分数为95.8%。对于四级分类系统，窦性心律的召回率、精确率和F1分数分别为97.6%、96.5%、97.0%；心房颤动为96.7%、96.9%、96.8%；房性早搏为90.5%、89.4%、89.9%；室性早搏为86.1%、89.6%、87.8%。四级分类系统的宏F1分数为92.9%。
嵌入智能可穿戴设备中的单导联心电图算法在检测心房颤动、房性/室性早搏和窦性心律方面表现出良好性能，因此将有助于心房颤动的筛查和管理。
