Reliable Detection of Atrial Fibrillation with a Medical Wearable during Inpatient Conditions
Malte Jacobsen,
Till A. Dembek,
Athanasios-Panagiotis Ziakos,
Rahil Gholamipoor,
Guido Kobbe,
Markus Kollmann,
Christopher Blum,
Dirk Müller-Wieland,
Andreas Napp,
Lutz Heinemann,
Nikolas Deubner,
Nikolaus Marx,
Stefan Isenmann,
Melchior Seyfarth
Affiliations
Malte Jacobsen
Faculty of Health, University Witten/Herdecke, 58448 Witten, Germany
Till A. Dembek
Department of Neurology, Faculty of Medicine, University of Cologne, 50937 Cologne, Germany
Athanasios-Panagiotis Ziakos
Faculty of Health, University Witten/Herdecke, 58448 Witten, Germany
Rahil Gholamipoor
Department of Computer Science, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
Guido Kobbe
Department of Hematology, Oncology, and Clinical Immunology, University Hospital Düsseldorf, Medical Faculty, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
Markus Kollmann
Department of Biology, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
Christopher Blum
Department of Biology, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
Dirk Müller-Wieland
Department of Internal Medicine I, University Hospital Aachen, RWTH Aachen University, 52074 Aachen, Germany
Andreas Napp
Department of Internal Medicine I, University Hospital Aachen, RWTH Aachen University, 52074 Aachen, Germany
Lutz Heinemann
Science-Consulting in Diabetes, 41462 Neuss, Germany
Nikolas Deubner
Faculty of Health, University Witten/Herdecke, 58448 Witten, Germany
Nikolaus Marx
Department of Internal Medicine I, University Hospital Aachen, RWTH Aachen University, 52074 Aachen, Germany
Stefan Isenmann
Faculty of Health, University Witten/Herdecke, 58448 Witten, Germany
Melchior Seyfarth
Faculty of Health, University Witten/Herdecke, 58448 Witten, Germany
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 aims 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.
