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Synthetic electrocardiograms for Brugada syndrome: from data generation to expert cardiologists evaluation

Zanchi, B; Monachino, G; Faraci, FD; Metaldi, M; Brugada, P; Sarquella-Brugada, G; Behr, ER; Brugada, J; Crotti, L; Belhassen, B; et al. Zanchi, B; Monachino, G; Faraci, FD; Metaldi, M; Brugada, P; Sarquella-Brugada, G; Behr, ER; Brugada, J; Crotti, L; Belhassen, B; Conte, G (2025) Synthetic electrocardiograms for Brugada syndrome: from data generation to expert cardiologists evaluation. EUROPEAN HEART JOURNAL - DIGITAL HEALTH. ISSN 2634-3916 https://doi.org/10.1093/ehjdh/ztaf039
SGUL Authors: Behr, Elijah Raphael

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Abstract

Aims Synthetic electrocardiograms (ECGs) for inherited cardiac diseases may overcome the issue related to data scarcity for artificial intelligence (AI)-based algorithms. This study aimed to evaluate experienced cardiologists’ ability to differentiate synthetic and real Brugada ECGs. Methods and results A total of 2244 ECG instances (50% synthetic generated by a generative adversarial network, 50% real Brugada patients’ ECGs) were evaluated by 7 cardiologists, each with >15 years of experience. All ECGs were standard 12-lead recordings acquired with identical settings (paper speed 25 mm/s, amplitude 10 mm/mV) and randomly assigned without identifying markers. The examination was blinded and conducted in 2 rounds with at least 2 h gap between rounds to assess potential learning effects and intra-rater reliability. Each physician classified the recordings as ‘real’ or ‘synthetic’ without having any additional information. Performance metrics, including accuracy, sensitivity, specificity, and intra-rater reliability (Cohen's Kappa), were analyzed. Brugada syndrome (BrS) specialists’ repeated evaluations were characterized by low accuracy (first round 40%, second round 42%), specificity (first round 22%, second round 26%) and sensitivity (first round 58%, second round 58%). Intra-rater reliability varied widely (Cohen’s Kappa: −0.12 to 0.80). Conclusion Synthetic Brugada ECGs cannot be adequately distinguished from real patients’ ECGs by BrS specialists.

Item Type: Article
Additional Information: © The Author(s) 2025. Published by Oxford University Press on behalf of the European Society of Cardiology. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.
Keywords: Brugada Syndrome, Synthetic ECG, Artificial Intelligence, Machine learning, AI-enabled ECG, Cardiogenetics
SGUL Research Institute / Research Centre: Academic Structure > Cardiovascular & Genomics Research Institute
Academic Structure > Cardiovascular & Genomics Research Institute > Clinical Cardiology
Journal or Publication Title: EUROPEAN HEART JOURNAL - DIGITAL HEALTH
ISSN: 2634-3916
Language: en
Publisher License: Creative Commons: Attribution-Noncommercial 4.0
Projects:
Project IDFunderFunder ID
PZ00P3_180055Swiss National Science Foundationhttp://dx.doi.org/10.13039/501100001711
URI: https://openaccess.sgul.ac.uk/id/eprint/117527
Publisher's version: https://doi.org/10.1093/ehjdh/ztaf039

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