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AI-Enabled ECG Combined with Dry Electrode Sensors for Population-Based Screening of Atrial Fibrillation

  • This study assessed the performance of a deep neural network (PulseAI, Belfast, United Kingdom) used in conjunction with a dry-electrode ECG sensor device (RhythmPad, D&FT, United Kingdom) to detect AF automatically. Simultaneous pairs of 12-lead ECGs and single-lead dry-electrode ECGs were collected from 622 patients. The 12-lead ECGs were manually overread and used as reference diagnoses. Twenty-two patients were confirmed with AF and had an interpretable 12-lead and single-lead dry-electrode ECG recording. The deep neural network analysed the dry-electrode ECGs, and performance was compared to the 12-lead interpretation. Overall, the deep neural network algorithm yielded a sensitivity of 96% (95% CI, 87%-100%), specificity of 99% (95% CI, 98%-100%) and positive predictive value of 81% (95% CI, 66%-96%) for detection of AF episodes. When coupled with dry-electrode ECG sensors, the PulseAI neural network allows for large-scale and low-cost screening for AF. Widespread implementation of this technology may allow for earlier detection,This study assessed the performance of a deep neural network (PulseAI, Belfast, United Kingdom) used in conjunction with a dry-electrode ECG sensor device (RhythmPad, D&FT, United Kingdom) to detect AF automatically. Simultaneous pairs of 12-lead ECGs and single-lead dry-electrode ECGs were collected from 622 patients. The 12-lead ECGs were manually overread and used as reference diagnoses. Twenty-two patients were confirmed with AF and had an interpretable 12-lead and single-lead dry-electrode ECG recording. The deep neural network analysed the dry-electrode ECGs, and performance was compared to the 12-lead interpretation. Overall, the deep neural network algorithm yielded a sensitivity of 96% (95% CI, 87%-100%), specificity of 99% (95% CI, 98%-100%) and positive predictive value of 81% (95% CI, 66%-96%) for detection of AF episodes. When coupled with dry-electrode ECG sensors, the PulseAI neural network allows for large-scale and low-cost screening for AF. Widespread implementation of this technology may allow for earlier detection, treatment, and management of patients with AF.show moreshow less

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Author:Daniel GuldenringORCiD, Alan KennedyORCiD, Dewar D. FinlayORCiD, Raymond R. BondORCiD, James D. McLaughlinORCiD, Chris Crockford
URL / DOI:https://ieeexplore.ieee.org/document/10081844
DOI:https://doi.org/10.22489/CinC.2022.312
Identifier:979-8-3503-0097-0 OPAC HS OPAC extern
Identifier:2325-887X OPAC HS OPAC extern
Parent Title (English):2022 Computing in Cardiology (CinC)
Publisher:IEEE
Document Type:conference proceeding (article)
Conference:2022 Computing in Cardiology (CinC), 04-07 September 2022, Tampere, Finland
Language:English
Date of Publication (online):2023/04/03
Year of first Publication:2023
Tag:Artificial intelligence in medicine; Biomedical Signal Processing; ECG
GND Keyword:Elektrokardiogramm
Volume:Vol. 49
Number of pages:4 Seiten
Institutes:Fakultät Elektrotechnik
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke
6 Technik, Medizin, angewandte Wissenschaften
Research focus:Eigenständige Forschungsfelder
Publication Lists:Güldenring, Daniel
Publication reviewed:begutachtet
Release Date:2023/04/19
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