Dunn, AnthonyElrefai, MohamedRoberts, PaulConiglio, StefanoWiles, BenedictZemkoho, Alain2024-08-222024-08-222021-09Dunn, A, Elrefai, M, Roberts, P, Coniglio, S, Wiles, B & Zemkoho, A 2021, 'Deep learning methods for screening patients' S-ICD implantation eligibility.', Artificial Intelligence in Medicine, vol. 119, 102139. https://doi.org/10.1016/j.artmed.2021.1021390933-3657ORCID: /0000-0002-1226-5335/work/100738106https://hdl.handle.net/2164/24094Acknowledgments The work of Anthony J. Dunn is jointly funded by Decision Analysis Services Ltd. and EPSRC through the Studentship with Reference EP/R513325/1. The work of Alain B. Zemkoho is supported by the EPSRC grant EP/V049038/1 and the Alan Turing Institute under the EPSRC grant EP/N510129/1. The feedback provided by Sion Cave (DAS Ltd) on the initial draft of the paper is gratefully acknowledged.122889864engSubcutaneous implantable cardioverter-defibrillatorsSudden cardiac deathVentricular arrhythmiaElectrocardiogramDeep learningConvolutional neural networksPhase space reconstructionPatient screeningRC Internal medicineEngineering and Physical Sciences Research Council (EPSRC)EP/R513325/1EP/V049038/1EP/N510129/1Supplementary DataRCJournal article10.1016/j.artmed.2021.102139https://doi.org/10.1016/j.artmed.2021.102139119