Heart Rhythm Classification from Electrocardiogram Signals Using Hybrid PSO-Neural Network Method and Neural ICA

Miftah Rahmalia Ariyati, Aulia Nasution

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

Abstract

Studies on the classification of heart rhythms from Electrocardiogram (ECG) signal interpretation have been widely reported. Several techniques for recognizing the abnormalities on left bundle branch (LBBB), right bundle branch (RBBB) and premature ventricular contraction (PVC) using the Taguchi optimization method and the Naïve Bayes classification method have been reported. Unfortunately results from the Naïve Bayes classification method are not as good as those using method such as SVM classification method. In the paper we propose a Hybrid PSO-Neural Network (NN) as a classification method and a Neural Independent Component Analysis (Neural-ICA) as a filter method. Neural ICA aims to separate the original signal and the noise signal on the ECG signal record. In this research the ICA method implements the Neural algorithm for the process of updating the weights after filter process. The Hybrid PSO-Neural Network is a Neural Network method that optimized by PSO to optimize the classification result. Hybrid PSO-NN method can improve the classification accuracy up to 2%, i.e. 99% accuracy, in comparison to NN method 98% accuracy and SVM method 96% accuracy, respectively.

Original languageEnglish
Title of host publicationProceeding - 2018 International Seminar on Intelligent Technology and Its Application, ISITIA 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages449-454
Number of pages6
ISBN (Electronic)9781538676547
DOIs
Publication statusPublished - 2 Jul 2018
Event2018 International Seminar on Intelligent Technology and Its Application, ISITIA 2018 - Bali, Indonesia
Duration: 30 Aug 201831 Aug 2018

Publication series

NameProceeding - 2018 International Seminar on Intelligent Technology and Its Application, ISITIA 2018

Conference

Conference2018 International Seminar on Intelligent Technology and Its Application, ISITIA 2018
Country/TerritoryIndonesia
CityBali
Period30/08/1831/08/18

Keywords

  • ECG signal classification
  • Independent Component Analysis
  • Neural Network
  • Particle Swarm Optimization

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