ECG-based machine learning model for AF identification in patients with first ischemic stroke
The CNN model effectively identifies AF in stroke patients using 12-lead ECGs and predicts future AF events, facilitating early anticoagulation therapy and potentially reducing recurrent stroke risk. Further prospective studies are warranted to confirm these findings.
Authors
- Chen Lin (ORCID: https://orcid.org/0000-0001-6261-8509)
- Lian‐Yu Lin (ORCID: https://orcid.org/0000-0001-7505-6429)
- Men‐Tzung Lo (ORCID: https://orcid.org/0000-0003-2630-4945)
- Yenn‐Jiang Lin (ORCID: https://orcid.org/0000-0001-8395-1052)
- Chih‐Chieh Yu (ORCID: https://orcid.org/0000-0002-2136-2444)
- Chih‐Min Liu (ORCID: https://orcid.org/0000-0002-7235-273X)
- Chia‐Hsin Chiang (ORCID: https://orcid.org/0000-0002-7068-0473)
- Yuqi Peng (ORCID: https://orcid.org/0009-0007-2879-6230)
Institutions
- National Yang Ming Chiao Tung University (TW)
- National Taiwan University (TW)
- National Central University (TW)
- Taipei Veterans General Hospital (TW)
- Taichung Veterans General Hospital (TW)
- National Taiwan University Hospital (TW)
Publication Details
- Journal
- International Journal of Stroke
- Published
- 2024-11-13
- DOI
- https://doi.org/10.1177/17474930241302272
- Citations
- 6
- Primary Topic
- Atrial Fibrillation Management and Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 2.41