Atrial fibrillation (AF) is the most common arrhythmia and has a major impact on morbidity and mortality; however, detection of asymptomatic AF is challenging. This study sims to evaluate the sensitivity and specificity of non-invasive AF detection by a medical wearable. In this observational trial, patients with AF admitted to a hospital carried the wearable and an ECG Holter (control) in parallel over a period of 24 h, while not in a physically restricted condition. The wearable with a tight-fit upper armband employs a photoplethysmography technology to determine pulse rates and inter-beat intervals. Different algorithms (including a deep neural network) were applied to five-minute periods photoplethysmography datasets for the detection of AF. A total of 2306 h of parallel recording time could be obtained in 102 patients; 1781 h (77.2%) were automatically interpretable by an algorithm. Sensitivity to detect AF was 95.2% and specificity 92.5% (area under the receiver operating characteristics curve (AUC) 0.97). Usage of deep neural network improved the sensitivity of AF detection by 0.8% (96.0%) and specificity by 6.5% (99.0%) (AUC 0.98). Detection of AF by means of a wearable is feasible in hospitalized but physically active patients. Employing a deep neural network enables reliable and continuous monitoring of AF.

中文翻译：

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在住院期间可穿戴医疗设备轻松检测房颤

心房颤动（AF）是最常见的心律不齐，对发病率和死亡率有重大影响；然而，无症状房颤的检测具有挑战性。这项研究模拟了通过医用可穿戴设备评估非侵入性AF检测的敏感性和特异性。在这项观察性试验中，入院的AF患者在24小时内平行携带了可穿戴设备和ECG动态心电图（对照），而身体不受限制。具有紧身上臂的可穿戴设备采用光电容积描记技术来确定脉搏频率和心跳间隔。将不同的算法（包括深度神经网络）应用于五分钟的光电容积描记数据集，以检测房颤。102名患者可获得总计2306小时的平行记录时间；1781小时（77。2％）可以通过算法自动解释。检测AF的敏感性为95.2％，特异性为92.5％（接受者工作特征曲线（AUC）下的面积为0.97）。使用深度神经网络可将AF检测的灵敏度提高0.8％（96.0％），并将特异性提高6.5％（99.0％）（AUC 0.98）。通过可穿戴设备检测房颤在住院但身体活跃的患者中是可行的。使用深度神经网络可以对AF进行可靠且连续的监控。通过可穿戴设备检测房颤在住院但身体活跃的患者中是可行的。使用深度神经网络可以对AF进行可靠且连续的监控。通过可穿戴设备检测房颤在住院但身体活跃的患者中是可行的。使用深度神经网络可以对AF进行可靠且连续的监控。