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.…
Author: | Daniel GuldenringORCiD, Alan KennedyORCiD, Dewar D. FinlayORCiD, Raymond R. BondORCiD, James D. McLaughlinORCiD, Chris Crockford |
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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 |