Adaptive Multilayer Generalized Learning Vector Quantization (AMGLVQ) as new algorithm with integrating feature extraction and classification for Arrhythmia heartbeats classification

Elly Matul Imah, Wisnu Jatmiko, T. Basaruddin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Citations (Scopus)

Abstract

Electrocardiogram (ECG) plays an important role in monitoring and preventing heart attacks. In this paper, we propose a new method Adaptive Multilayer Generalized Learning Vector Quantization (AMGLVQ) that integrated feature extraction and classification for the automatic classification of heartbeats in an ECG signal. Since this task has specific characteristics such as, inconsistency optimization on feature extraction and classification, unclassifiable beats and a strong class unbalance, so in this study we proposed new algorithm to handle the problems. The algorithm will be evaluated on real ECG signals from the MIT arrhythmia database. The Experiments show that the proposed method can improve the accuracy of classification better than SVM or back-propagation NN and also able to handle some problems of heartbeat classification: unbalance class, inconsistency between feature extraction and classification and detecting unknown beat on testing phase.

Original languageEnglish
Title of host publicationProceedings 2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012
Pages150-155
Number of pages6
DOIs
Publication statusPublished - 1 Dec 2012
Event2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012 - Seoul, Korea, Republic of
Duration: 14 Oct 201217 Oct 2012

Publication series

NameConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
ISSN (Print)1062-922X

Conference

Conference2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012
Country/TerritoryKorea, Republic of
CitySeoul
Period14/10/1217/10/12

Keywords

  • AMGLVQ
  • arrhythmia
  • back-propagation
  • ECG
  • SVM

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