Correlation analysis of deep learning methods in S-ICD screening.

Journal: Annals of noninvasive electrocardiology : the official journal of the International Society for Holter and Noninvasive Electrocardiology, Inc
Published Date:

Abstract

BACKGROUND: Machine learning methods are used in the classification of various cardiovascular diseases through ECG data analysis. The concept of varying subcutaneous implantable cardiac defibrillator (S-ICD) eligibility, owing to the dynamicity of ECG signals, has been introduced before. There are practical limitations to acquiring longer durations of ECG signals for S-ICD screening. This study explored the potential use of deep learning methods in S-ICD screening.

Authors

  • Mohamed ElRefai
    Cardiac Rhythm Management Research Department, University Hospital Southampton NHS Foundation Trust, Southampton, UK.
  • Mohamed Abouelasaad
    Cardiac Rhythm Management Research Department, University Hospital Southampton NHS Foundation Trust, Southampton, UK.
  • Benedict M Wiles
    St George's University Hospitals NHS Foundation Trust, United Kingdom.
  • Anthony J Dunn
    University of Southampton, School of Mathematical Sciences, United Kingdom.
  • Stefano Coniglio
    University of Southampton, School of Mathematical Sciences, United Kingdom.
  • Alain B Zemkoho
    University of Southampton, School of Mathematical Sciences, United Kingdom. Electronic address: a.b.zemkoho@soton.ac.uk.
  • John Morgan
    Faculty of Medicine, University of Southampton, Southampton, UK.
  • Paul R Roberts
    University Hospital of Southampton, United Kingdom.