Early prediction of sudden cardiac death using multimodal fusion of ECG Features extracted from Hilbert-Huang and wavelet transforms with explainable vision transformer and CNN models.

Journal: Computer methods and programs in biomedicine
PMID: 39447439

Abstract

BACKGROUND AND OBJECTIVE: Sudden cardiac death (SCD) is a critical health issue characterized by the sudden failure of heart function, often caused by ventricular fibrillation (VF). Early prediction of SCD is crucial to enable timely interventions. However, current methods predict SCD only a few minutes before its onset, limiting intervention time. This study aims to develop a deep learning-based model for the early prediction of SCD using electrocardiography (ECG) signals.

Authors

  • Hardik Telangore
    Department of Electrical and Computer Science Engineering, Institute of Infrastructure, Technology, Research and Management (IITRAM), Ahmedabad, India. Electronic address: telangore.hardik.23pe@iitram.ac.in.
  • Victor Azad
    Department of Electrical and Computer Science Engineering, Institute of Infrastructure, Technology, Research and Management (IITRAM), Ahmedabad, Gujarat, 380026, India. Electronic address: victor.azad.20e@iitram.ac.in.
  • Manish Sharma
    Department of Electrical Engineering, Institute of Infrastructure Technology Research and Management, Ahmedabad, India. Electronic address: manishsharma.iitb@gmail.com.
  • Ankit Bhurane
    Department of Electronics and Communication Engineering, Visvesvaraya National Institute of Technology (VNIT), Nagpur, 440010, Maharashtra, India. Electronic address: ankitbhurane@ece.vnit.ac.in.
  • Ru San Tan
    Department of Cardiology, National Heart Centre, Singapore.
  • U Rajendra Acharya
    School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Darling Heights, Australia.

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