A Novel Deep Ensemble Method for Selective Classification of Electrocardiograms.

Journal: IEEE transactions on bio-medical engineering
Published Date: Jan 21, 2025

Abstract

OBJECTIVE: Telehealth paradigms are essential for remotely managing patients with chronic conditions. To assist clinicians in handling the large volumes of data collected through these systems, clinical decision support systems (CDSSs) have been developed. However, the effectiveness of CDSSs depends on the quality of remotely recorded physiological data and the reliability of the algorithms used for processing this data. This study aims to reliably detect atrial fibrillation (AF) from short-term single-lead (STSL) electrocardiogram (ECG) recordings obtained in unsupervised telehealth environments.

Authors

  • Ahmadreza Argha
  • Hamid Alinejad-Rokny
    Systems Biology and Health Data Analytics Lab, The Graduate School of Biomedical Engineering, UNSW Sydney, 2052 Sydney, Australia; School of Computer Science and Engineering, The University of New South Wales (UNSW Sydney), 2052 Sydney, Australia; Health Data Analytics Program Leader, AI-enabled Processes (AIP) Research Centre, Macquarie University, Sydney 2109, Australia.
  • Martin Baumgartner
    Paediatric Neuro-Oncology Research Group, Department of Oncology, Children's Research Center, University Children's Hospital Zürich, Lengghalde 5, 8008, Zürich, Switzerland.
  • Günter Schreier
    AIT Austrian Institute of Technology, Austria.
  • Branko G Celler
  • Stephen J Redmond
    Graduate School of Biomedical Engineering, University of New South Wales, Sydney 2033, Australia.
  • Ken Butcher
  • Sze-Yuan Ooi
  • Nigel H Lovell

Keywords

External Resources

Popular Topics
Recent Journals