GenECG: a synthetic image-based ECG dataset to augment artificial intelligence-enhanced algorithm development
GenECG is the first synthetic image-based ECG dataset to pass a clinical Turing test. The dataset will enable image-based AI-ECG algorithm development, ensuring utility in low resource areas, prehospital settings and hospital environments where signal data are unavailable.
Authors
- Steven Niederer (ORCID: https://orcid.org/0000-0002-4612-6982)
- Adam Barton (ORCID: https://orcid.org/0000-0001-9786-0936)
- Ali Gharaviri (ORCID: https://orcid.org/0000-0003-1803-8237)
- Miguel O. Bernabéu (ORCID: https://orcid.org/0000-0002-6456-3756)
- Kyaw Soe Tun (ORCID: https://orcid.org/0009-0000-6921-3491)
- Mark O’Neill (ORCID: https://orcid.org/0000-0002-6070-7177)
- Rachel Burns (ORCID: https://orcid.org/0000-0002-9559-2867)
- Malihe Javidi (ORCID: https://orcid.org/0000-0002-6854-9097)
- Darwon Rashid
- Steven E. Williams (ORCID: https://orcid.org/0000-0002-7266-2084)
- Neil Bodagh (ORCID: https://orcid.org/0000-0001-5475-5298)
- Irum Kotadia (ORCID: https://orcid.org/0000-0003-1825-4415)
- Vinush Vigneswaran
- Magdalena Kliś
Institutions
- King's College London (GB)
- Guy's and St Thomas' NHS Foundation Trust (GB)
- Royal Bank of Scotland (United Kingdom) (GB)
- King's College School (GB)
- University of Edinburgh (GB)
Publication Details
- Journal
- BMJ Health & Care Informatics
- Published
- 2025-05-01
- DOI
- https://doi.org/10.1136/bmjhci-2024-101335
- Citations
- 3
- Primary Topic
- ECG Monitoring and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 2.78
Funders
- Wellcome Trust
- British Heart Foundation
- Royal College of Surgeons of Edinburgh
- Medical Research Council
- Engineering and Physical Sciences Research Council