An automated ECG-based deep learning for the early-stage identification and classification of cardiovascular disease.

Journal: Technology and health care : official journal of the European Society for Engineering and Medicine
Published Date: Jan 1, 2024

Abstract

BACKGROUND: Heart disease represents the leading cause of death globally. Timely diagnosis and treatment can prevent cardiovascular issues. An Electrocardiograms (ECG) serves as a diagnostic tool for identifying heart difficulties. Cardiovascular Disease (CVD) often gets identified through ECGs. Deep learning (DL) garners attention in healthcare due to its potential in swiftly diagnosing ECG anomalies, crucial for patient monitoring. Conversely, automatic CVD detection from ECGs poses a challenging task, wherein rule-based diagnostic models usually achieve top-notch performance. These models encounter complications in supervision vast volumes of diverse data, demanding widespread analysis and medical capability to ensure precise CVD diagnosis.

Authors

  • Anand Pandey
    Department of Computer Science and Application, SSET, Sharda University, Greater Noida, India.
  • Ajeet Singh
    Department of Computer Science and Engineering, Moradabad Institute of Technology, Moradabad, India.
  • Prasanthi Boyapati
    Department of Computer Science and Engineering, School of Engineering and Sciences, SRM University, Mangalagiri Mandal, India.
  • Abhay Chaturvedi
    Department of Electronics and Communication Engineering, GLA University, Mathura, India.
  • N Purushotham
    Department of Computer Science and Engineering, School of Computing, Mohan Babu University, Tirupati, India.
  • Sangeetha M
    Department of Computer Science and Engineering, Panimalar Engineering College, Chennai, India.

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