Classification of Multichannel ECG Signals Using a Cross-Distance Analysis

Abstract

This paper presents a multi-stage algorithm for multi-channel ECG beat classification into normal and abnormal categories using a sequential beat clustering and a cross- distance analysis algorithm. After clustering stage, a search algorithm is applied to detect the main normal class. Then other clusters are classified based on their distance from the main normal class. The algorithm is developed for both 1-lead and 2- lead ECG. Evaluated results on MIT-BIH database exhibit a classification error of less than 1% for 1-lead and 0.2% for 2- lead and clustering error of 0,2%.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Oct 25, 2001
Accession Number
ADA411659

Entities

People

  • Kambiz Nayebi
  • Morteza Shahram

Organizations

  • Sharif University of Technology

Tags

Communities of Interest

  • C4I
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Cardiac Arrhythmias
  • Classification
  • Clustering
  • Computational Complexity
  • Databases
  • Detection
  • Electrical Engineering
  • Engineering
  • Feature Selection
  • Frequency
  • Frequency Domain
  • Machine Learning
  • Military Research
  • Monitoring
  • Neural Networks

Fields of Study

  • Computer science

Readers

  • Brain and Cognitive Science; Experimental Psychology; Cognitive Neuroscience
  • Neural Network Machine Learning.