Improving myocardial infarction diagnosis with Siamese network-based ECG analysis.

Journal: PloS one
PMID: 39883662

Abstract

BACKGROUND: Heart muscle damage from myocardial infarction (MI) is brought on by insufficient blood flow. The leading cause of death for middle-aged and older people worldwide is myocardial infarction (MI), which is difficult to diagnose because it has no symptoms. Clinicians must evaluate electrocardiography (ECG) signals to diagnose MI, which is difficult and prone to observer bias. To be effective in actual practice, an automated, and computerized detection system for Myocardial Infarction using ECG images, must meet a number of criteria.

Authors

  • Vaibhav Gadag
    School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India.
  • Simrat Singh
    School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India.
  • Anshul Harish Khatri
    School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India.
  • Shruti Mishra
    Department of Radiology, Washington University in St. Louis, School of Medicine, 510 S. Kingshighway, MC 8131, Saint Louis, MO 63110, USA.
  • Sandeep Kumar Satapathy
    Department of Computer Science, Yonsei University, Seodaemun, Seoul, 03722, South Korea.
  • Sung-Bae Cho
    Department of Computer Science, Yonsei University, Seoul, South Korea. Electronic address: sbcho@cs.yonsei.ac.kr.
  • Abishi Chowdhury
    School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India.
  • Amrit Pal
    School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India.
  • Sachi Nandan Mohanty
    Department of Computer Science & Engineering, Vardhaman College of Engineering (Autonomous), Hyderabad 501218, India.

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