TY - GEN
T1 - ArNet-ECG
T2 - 2022 Computing in Cardiology, CinC 2022
AU - Ben-Moshe, Noam
AU - Biton, Shany
AU - Behar, Joachim A.
N1 - Publisher Copyright:
© 2022 Creative Commons.
PY - 2022
Y1 - 2022
N2 - Introduction: Atrial fibrillation (AF) is the most prevalent heart arrhythmia. AF manifests on the electrocar-diogram (ECG) though irregular beat-to-beat time interval variation, the absence of P-wave and the presence of fibrillatory waves (f-wave). We hypothesize that a deep learning (DL) approach trained on the raw ECG will enable robust detection of AF events and the estimation of the AF burden (AFB). We further hypothesize that the performance reached leveraging the raw ECG will be superior to previously developed methods using the beat-to-beat interval variation time series. Consequently, we develop a new DL algorithm, denoted ArNet-ECG, to robustly detect AF events and estimate the AFB from the raw ECG and bench-mark this algorithms against previous work. Methods: A dataset including 2,247 adult patients and totaling over 53,753 hours of continuous ECG from the University of Virginia (UVAF) was used. Results: ArNet-ECG obtained an F1 of 0.96 and ArNet2 obtained an F1 0.94. Discussion and conclusion: ArNet-ECG outperformed ArNet2 thus demonstrating that using the raw ECG provides added performance over the beat-to-beat interval time series. The main reason found for explaining the higher performance of ArNet-ECG was its high performance on atrial flutter examples versus poor performance on these recordings for ArNet2.
AB - Introduction: Atrial fibrillation (AF) is the most prevalent heart arrhythmia. AF manifests on the electrocar-diogram (ECG) though irregular beat-to-beat time interval variation, the absence of P-wave and the presence of fibrillatory waves (f-wave). We hypothesize that a deep learning (DL) approach trained on the raw ECG will enable robust detection of AF events and the estimation of the AF burden (AFB). We further hypothesize that the performance reached leveraging the raw ECG will be superior to previously developed methods using the beat-to-beat interval variation time series. Consequently, we develop a new DL algorithm, denoted ArNet-ECG, to robustly detect AF events and estimate the AFB from the raw ECG and bench-mark this algorithms against previous work. Methods: A dataset including 2,247 adult patients and totaling over 53,753 hours of continuous ECG from the University of Virginia (UVAF) was used. Results: ArNet-ECG obtained an F1 of 0.96 and ArNet2 obtained an F1 0.94. Discussion and conclusion: ArNet-ECG outperformed ArNet2 thus demonstrating that using the raw ECG provides added performance over the beat-to-beat interval time series. The main reason found for explaining the higher performance of ArNet-ECG was its high performance on atrial flutter examples versus poor performance on these recordings for ArNet2.
UR - http://www.scopus.com/inward/record.url?scp=85152896426&partnerID=8YFLogxK
U2 - 10.22489/CinC.2022.061
DO - 10.22489/CinC.2022.061
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AN - SCOPUS:85152896426
T3 - Computing in Cardiology
BT - 2022 Computing in Cardiology, CinC 2022
Y2 - 4 September 2022 through 7 September 2022
ER -