Machine learning and the electrocardiogram over two decades: Time series and meta-analysis of the algorithms, evaluation metrics and applications.

Journal: Artificial intelligence in medicine
Published Date: Aug 27, 2022

Abstract

BACKGROUND: The application of artificial intelligence to interpret the electrocardiogram (ECG) has predominantly included the use of knowledge engineered rule-based algorithms which have become widely used today in clinical practice. However, over recent decades, there has been a steady increase in the number of research studies that are using machine learning (ML) to read or interrogate ECG data.

Authors

  • Khaled Rjoob
    Faculty of Computing, Engineering & Built Environment, Ulster University, UK. Electronic address: rjoob-k@ulster.ac.uk.
  • Raymond Bond
    Ulster University, School of Computing, York St, Northern Ireland.
  • Dewar Finlay
    Nanotechnology and Integrated Bioengineering Centre, Ulster University, Jordanstown, Northern Ireland, United Kingdom.
  • Victoria McGilligan
    Faculty of Life & Health Sciences, Centre for Personalised Medicine, Ulster University, UK.
  • Stephen J Leslie
    Department of Diabetes & Cardiovascular Science, University of the Highlands and Islands, Centre for Health Science, Inverness, UK.
  • Ali Rababah
    Faculty of Computing, Engineering & Built Environment, Ulster University, UK.
  • Aleeha Iftikhar
    Faculty of Computing, Engineering & Built Environment, Ulster University, UK.
  • Daniel Guldenring
    HTW Berlin, Wilhelminenhofstr. 75A, 12459 Berlin, Germany.
  • Charles Knoery
    Department of Diabetes & Cardiovascular Science, University of the Highlands and Islands, Centre for Health Science, Inverness, UK.
  • Anne McShane
    Emergency Department, Letterkenny University Hospital, Donegal, Ireland.
  • Aaron Peace
    Western Health and Social Care Trust, C-TRIC, Ulster University, UK.
  • Peter W Macfarlane
    University of Glasgow, Glasgow, Scotland.

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