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.
|Title of host publication||2022 Computing in Cardiology, CinC 2022|
|Publisher||IEEE Computer Society|
|Number of pages||4|
|Publication status||Published online - 3 Apr 2023|
|Event||2022 Computing in Cardiology, CinC 2022 - Tampere, Finland|
Duration: 4 Sept 2022 → 7 Sept 2022
|Name||Computing in Cardiology|
|Conference||2022 Computing in Cardiology, CinC 2022|
|Period||4/09/22 → 7/09/22|
Bibliographical noteFunding Information:
Funding to present this research was provided by the European Union's INTERREG VA Programme, managed by the Special EU Programmes Body (SEUPB), which is part of the Eastern Corridor Medical Engineering centre (ECME).
© 2022 Creative Commons.
- Deep learning
- Performance evaluation
- Neural networks