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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,...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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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. |
format | Online Article Text |
id | pubmed-7583973 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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