A Recognition of ECG Arrhythmias Using Artificial Neural Networks

Abstract

In this study, Artificial Neural Networks (ANN) has been used to classify the ECG arrhythmias. Types of arrhythmias chosen from MIT-BIH ECG database to train ANN include normal sinus rhythm, sinus bradycardia, ventricular tachycardia, sinus arrhythmia, atrial premature contraction, paced beat, right bundle branch block, left bundle branch block, atrial fibrillation, and atrial flutter have been as. The different structures of ANN have been trained by arrhythmia separately and also by mixing these 10 different arrhythmias. The most appropriate ANN structure is used for each class to test patients' records. The ECG records of 17 patients whose average age is 38.6 were made in the Cardiology Department, Faculty of Medicine at Selcuk University. Forty-two different test patterns were extracted from these records. These patterns were tested with the most appropriate ANN structures of single classification case and mixed classification cases. The average error of single classifications was found to be 4.3% and the average error of mixed classification 2.2%.

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Document Details

Document Type
Technical Report
Publication Date
Oct 25, 2001
Accession Number
ADA410236

Entities

People

  • Bekir Karlik
  • Yueksel Oezbay

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Cardiac Arrest
  • Cardiac Arrhythmias
  • Cardiovascular System
  • Computers
  • Death
  • Diseases And Disorders
  • Electrocardiography
  • Health Services
  • Heart Diseases
  • Myocardial Ischemia
  • Neural Networks
  • Self Organizing Systems

Fields of Study

  • Medicine

Readers

  • Cardiovascular Physiology
  • Computer Vision.
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks