Reliable Detection of Atrial Fibrillation with a Medical Wearable during Inpatient Conditions

Sensors

Guido Kobbe¹  Markus Kollmann²  Christoph Blum²  Rahil Gholamipoor²  Nikolaus Marx³  Andreas Napp³  Dirk Müller-Wieland³  Till A. Dembek⁴  Nikolas Deubner⁵  Melchior Seyfarth⁵  Stefan Isenmann⁵  Malte Jacobsen⁵  Athanasios-Panagiotis Ziakos⁵  Lutz Heinemann⁶ 

[1] Department of Biology, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany;Department of Hematology, Oncology, and Clinical Immunology, University Hospital Düsseldorf, Medical Faculty, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany;Department of Internal Medicine I, University Hospital Aachen, RWTH Aachen University, 52074 Aachen, Germany;Department of Neurology, Faculty of Medicine, University of Cologne, 50937 Cologne, Germany;Faculty of Health, University Witten/Herdecke, 58448 Witten, Germany;Science-Consulting in Diabetes, 41462 Neuss, Germany;

关键词: clinical trial;    wearable sensors;    atrial fibrillation;    photoplethysmography;    deep neural network;   

DOI  :  10.3390/s20195517

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown