Integrating deep learning with ECG, heart rate variability and demographic data for improved detection of atrial fibrillation
AlexNet and VGG-16 excelled in AF detection, with HRV data improving sensitivity, and demographic data providing additional benefits. These results highlight the potential of multimodal approaches, pending further clinical validation.
Authors
- Aidin Rawshani (ORCID: https://orcid.org/0000-0002-4509-873X)
- Mats Börjesson (ORCID: https://orcid.org/0000-0002-8786-0438)
- Truls Råmunddal (ORCID: https://orcid.org/0000-0001-9483-5962)
- Jan Borén (ORCID: https://orcid.org/0000-0003-0786-8091)
- Johan Engdahl (ORCID: https://orcid.org/0000-0002-1677-7215)
- Zacharias Mandalenakis (ORCID: https://orcid.org/0000-0001-5093-9743)
- Elmir Ömerovic (ORCID: https://orcid.org/0000-0002-3875-8621)
- Araz Rawshani (ORCID: https://orcid.org/0000-0003-2066-3533)
- Antros Louca (ORCID: https://orcid.org/0009-0003-7699-593X)
- Vibha Gupta (ORCID: https://orcid.org/0000-0002-3785-8380)
- Eva A. Andersson (ORCID: https://orcid.org/0009-0001-0567-2020)
- Deepak L Bhatt
- Gustav Smith
Institutions
- Sahlgrenska University Hospital (SE)
- Institute of Molecular Medicine (IN)
- University of Gothenburg (SE)
- Icahn School of Medicine at Mount Sinai (US)
Publication Details
- Journal
- Open Heart
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1136/openhrt-2025-003185
- Citations
- 4
- Primary Topic
- ECG Monitoring and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 5.55
Funders
- Västra Götalandsregionen
- Knut och Alice Wallenbergs Stiftelse
- Göteborgs Universitet
- Wallenberg Centre for Molecular and Translational Medicine