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

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,...

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Autores principales: Jacobsen, Malte, Dembek, Till A., Ziakos, Athanasios-Panagiotis, Gholamipoor, Rahil, Kobbe, Guido, Kollmann, Markus, Blum, Christopher, Müller-Wieland, Dirk, Napp, Andreas, Heinemann, Lutz, Deubner, Nikolas, Marx, Nikolaus, Isenmann, Stefan, Seyfarth, Melchior
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7583973/
https://www.ncbi.nlm.nih.gov/pubmed/32993132
http://dx.doi.org/10.3390/s20195517
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author Jacobsen, Malte
Dembek, Till A.
Ziakos, Athanasios-Panagiotis
Gholamipoor, Rahil
Kobbe, Guido
Kollmann, Markus
Blum, Christopher
Müller-Wieland, Dirk
Napp, Andreas
Heinemann, Lutz
Deubner, Nikolas
Marx, Nikolaus
Isenmann, Stefan
Seyfarth, Melchior
author_facet Jacobsen, Malte
Dembek, Till A.
Ziakos, Athanasios-Panagiotis
Gholamipoor, Rahil
Kobbe, Guido
Kollmann, Markus
Blum, Christopher
Müller-Wieland, Dirk
Napp, Andreas
Heinemann, Lutz
Deubner, Nikolas
Marx, Nikolaus
Isenmann, Stefan
Seyfarth, Melchior
author_sort Jacobsen, Malte
collection PubMed
description 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.
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spelling pubmed-75839732020-10-29 Reliable Detection of Atrial Fibrillation with a Medical Wearable during Inpatient Conditions Jacobsen, Malte Dembek, Till A. Ziakos, Athanasios-Panagiotis Gholamipoor, Rahil Kobbe, Guido Kollmann, Markus Blum, Christopher Müller-Wieland, Dirk Napp, Andreas Heinemann, Lutz Deubner, Nikolas Marx, Nikolaus Isenmann, Stefan Seyfarth, Melchior Sensors (Basel) Article 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. MDPI 2020-09-26 /pmc/articles/PMC7583973/ /pubmed/32993132 http://dx.doi.org/10.3390/s20195517 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Jacobsen, Malte
Dembek, Till A.
Ziakos, Athanasios-Panagiotis
Gholamipoor, Rahil
Kobbe, Guido
Kollmann, Markus
Blum, Christopher
Müller-Wieland, Dirk
Napp, Andreas
Heinemann, Lutz
Deubner, Nikolas
Marx, Nikolaus
Isenmann, Stefan
Seyfarth, Melchior
Reliable Detection of Atrial Fibrillation with a Medical Wearable during Inpatient Conditions
title Reliable Detection of Atrial Fibrillation with a Medical Wearable during Inpatient Conditions
title_full Reliable Detection of Atrial Fibrillation with a Medical Wearable during Inpatient Conditions
title_fullStr Reliable Detection of Atrial Fibrillation with a Medical Wearable during Inpatient Conditions
title_full_unstemmed Reliable Detection of Atrial Fibrillation with a Medical Wearable during Inpatient Conditions
title_short Reliable Detection of Atrial Fibrillation with a Medical Wearable during Inpatient Conditions
title_sort reliable detection of atrial fibrillation with a medical wearable during inpatient conditions
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7583973/
https://www.ncbi.nlm.nih.gov/pubmed/32993132
http://dx.doi.org/10.3390/s20195517
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