Accurate deep neural network model to detect cardiac arrhythmia on more than 10,000 individual subject ECG records.

Journal: Computer methods and programs in biomedicine
Published Date: Sep 8, 2020

Abstract

BACKGROUND AND OBJECTIVE: Cardiac arrhythmia, which is an abnormal heart rhythm, is a common clinical problem in cardiology. Detection of arrhythmia on an extended duration electrocardiogram (ECG) is done based on initial algorithmic software screening, with final visual validation by cardiologists. It is a time consuming and subjective process. Therefore, fully automated computer-assisted detection systems with a high degree of accuracy have an essential role in this task. In this study, we proposed an effective deep neural network (DNN) model to detect different rhythm classes from a new ECG database.

Authors

  • Özal Yildirim
    Computer Engineering Department, Engineering Faculty, Munzur University, Tunceli, Turkey. Electronic address: oyildirim@munzur.edu.tr.
  • Muhammed Talo
    Department of Computer Engineering, Munzur University, Tunceli, Turkey.
  • Edward J Ciaccio
    Department of Medicine, Celiac Disease Center, Columbia University, New York, USA.
  • 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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