Detection and Classification of ECG Chaotic Components Using ANN Trained by Specially Simulated Data

Polina Kurtser, Ofer Levi, Vladimir Gontar

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

2 Scopus citations

Abstract

This paper presents the use of simulated ECG signals with known chaotic and random noise combination for training of an Artificial Neural Network (ANN) as a classification tool for analysis of chaotic ECG components. Preliminary results show about 85% overall accuracy in the ability to classify signals into two types of chaotic maps - logistic and Henon. Robustness to random noise is also presented. Future research in the form of raw data analysis is proposed, and further features analysis is needed.

Original languageEnglish
Title of host publicationEngineering Applications of Neural Networks - 13th International Conference, EANN 2012, Proceedings
EditorsShigang Yue, Lazaros Iliadis
Pages193-202
Number of pages10
DOIs
StatePublished - 1 Dec 2012
Event2012 International Conference on Artificial Intelligence and Computational Intelligence, AICI 2012 - Chengdu, China
Duration: 26 Oct 201228 Oct 2012

Publication series

NameCommunications in Computer and Information Science
Volume311
ISSN (Print)1865-0929

Conference

Conference2012 International Conference on Artificial Intelligence and Computational Intelligence, AICI 2012
Country/TerritoryChina
CityChengdu
Period26/10/1228/10/12

Keywords

  • Artificial Neural Networks
  • Deterministic chaos
  • ECG

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