TY - CONF A1 - Guldenring, Daniel A1 - Kennedy, Alan A1 - Finlay, Dewar D. A1 - Bond, Raymond R. A1 - McLaughlin, James D. A1 - Crockford, Chris T1 - AI-Enabled ECG Combined with Dry Electrode Sensors for Population-Based Screening of Atrial Fibrillation T2 - 2022 Computing in Cardiology (CinC) N2 - 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. KW - Elektrokardiogramm KW - Biomedical Signal Processing KW - ECG KW - Artificial intelligence in medicine Y1 - 2023 UR - https://opus4.kobv.de/opus4-hs-kempten/frontdoor/index/index/docId/1705 UR - https://ieeexplore.ieee.org/document/10081844 SN - 979-8-3503-0097-0 SN - 2325-887X VL - Vol. 49 PB - IEEE ER -