GenECG: a synthetic image-based ECG dataset to augment artificial intelligence-enhanced algorithm development.

Abstract

Authors

• Neil Bodagh
  King's College London, London, UK. neil.bodagh@kcl.ac.uk.
• Kyaw Soe Tun
  Guy's and St Thomas' NHS Foundation Trust, London, UK.
• Adam Barton
  Neurolabs, Edinburgh, UK.
• Malihe Javidi
  Computer Engineering Department, Quchan University of Technology, Quchan, Iran. Electronic address: m.javidi@qiet.ac.ir.
• Darwon Rashid
  Centre for Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, UK.
• Rachel Burns
  Centre for Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, UK.
• Irum Kotadia
  School of Biomedical Engineering and Imaging Sciences, King's College London, United Kingdom (C.H.R., I.S., J.Y., M.B., A.M., J.A.S.-L., I.K., J.W., C.C., O.R., M.O., S.E.W., S.A.N.).
• Magda Klis
  King's College London, London, UK.
• Ali Gharaviri
  University of Edinburgh, Edinburgh, UK.
• Vinush Vigneswaran
  University of Edinburgh, Edinburgh, UK.
• Steven Niederer
  Department of Biomedical Engineering, King's College London, United Kingdom (C.C., S.N.).
• Mark O'Neill
  Division of Cardiovascular Medicine, Guys and St Thomas' NHS Foundation Trust, King's College London, London, United Kingdom.
• Miguel O Bernabeu
  Usher Institute, University of Edinburgh, UK.
• Steven E Williams
  Biomedical Engineering and Imaging Sciences, King's College London, United Kingdom (O.R., I.S., C.H.R., R.K., H.C., J.W., L.O., R.M., M.O., S.E.W., S.N.).

Keywords

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