An effective ECG arrhythmia classification algorithm

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Citations (Scopus)

Abstract

This paper presents an effective electrocardiogram (ECG) arrhythmia classification scheme consisting of a feature reduction method combining principal component analysis (PCA) with linear discriminant analysis (LDA), and a probabilistic neural network (PNN) classifier to discriminate eight different types of arrhythmia from ECG beats. Each ECG beat sample composed of 200 sampling points at a 360 Hz sampling rate around an R peak is extracted from ECG signals. The feature reduction method is employed to find important features from ECG beats, and to improve the classification accuracy of the classifier. With the features, the PNN is then trained to serve as classifier for discriminating eight different types of ECG beats. The average classification accuracy of the proposed scheme is 99.71%. Our experimental results have successfully validated the integration of the PNN classifier with the proposed feature reduction method can achieve satisfactory classification accuracy.

Original languageEnglish
Title of host publicationBio-Inspired Computing and Applications - 7th International Conference on Intelligent Computing, ICIC 2011, Revised Selected Papers
Pages545-550
Number of pages6
DOIs
Publication statusPublished - 2011 Dec 1
Event7th International Conference on Intelligent Computing, ICIC 2011 - Zhengzhou, China
Duration: 2011 Aug 112011 Aug 14

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6840 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other7th International Conference on Intelligent Computing, ICIC 2011
CountryChina
CityZhengzhou
Period11-08-1111-08-14

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Theoretical Computer Science

Fingerprint Dive into the research topics of 'An effective ECG arrhythmia classification algorithm'. Together they form a unique fingerprint.

Cite this