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RawECGNet : Deep Le...
RawECGNet : Deep Learning Generalization for Atrial Fibrillation Detection From the Raw ECG
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- Ben-Moshe, Noam (författare)
- Technion - Israel Institute of Technology
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- Tsutsui, Kenta (författare)
- Saitama Medical Center
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- Biton, Shany (författare)
- Technion - Israel Institute of Technology
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- Zvuloni, Eran (författare)
- Technion - Israel Institute of Technology
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- Sornmo, Leif (författare)
- Lund University,Lunds universitet,Avdelningen för Biomedicinsk teknik,Institutionen för biomedicinsk teknik,Institutioner vid LTH,Lunds Tekniska Högskola,LTH profilområde: Teknik för hälsa,LTH profilområden,Department of Biomedical Engineering,Departments at LTH,Faculty of Engineering, LTH,LTH Profile Area: Engineering Health,LTH Profile areas,Faculty of Engineering, LTH
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- Behar, Joachim A. (författare)
- Technion - Israel Institute of Technology
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(creator_code:org_t)
- 2024
- 2024
- Engelska 10 s.
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Ingår i: IEEE Journal of Biomedical and Health Informatics. - 2168-2194. ; , s. 1-10
- Relaterad länk:
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http://dx.doi.org/10...
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https://lup.lub.lu.s...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Introduction Deep learning models for detecting episodes of atrial fibrillation (AF) using rhythm information in long-term ambulatory ECG recordings have shown high performance. However, the rhythm-based approach does not take advantage of the morphological information conveyed by the different ECG waveforms, particularly the f-waves. As a result, the performance of such models may be inherently limited. Methods: To address this limitation, we have developed a deep learning model, named RawECGNet, to detect episodes of AF and atrial flutter (AFl) using the raw, single-lead ECG. We compare the generalization performance of RawECGNet on two external data sets that account for distribution shifts in geography, ethnicity, and lead position. RawECGNet is further benchmarked against a state-of-the-art deep learning model, named ArNet2, which utilizes rhythm information as input. Results: Using RawECGNet, the results for the different leads in the external test sets in terms of the F1 score were 0.91–0.94 in RBDB and 0.93 in SHDB, compared to 0.89–0.91 in RBDB and 0.91 in SHDB for ArNet2. The results highlight RawECGNet as a high-performance, generalizable algorithm for detection of AF and AFl episodes, exploiting information on both rhythm and morphology.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Annan data- och informationsvetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Other Computer and Information Science (hsv//eng)
Nyckelord
- Atrial fibrillation
- atrial flutter
- Data models
- Deep learning
- deep learning
- Detectors
- electrocardiogram
- Electrocardiography
- Recording
- Rhythm
- Training
Publikations- och innehållstyp
- art (ämneskategori)
- ref (ämneskategori)
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