Neuro-fuzzy-based arrhythmia classification using heart rate variability features

Felipe Ramírez, HÉCTOR GABRIEL ALLENDE CID, Alejandro Veloz, Héctor Allende

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

4 Scopus citations

Abstract

Arrhythmia diagnosis is commonly conducted through visual analysis of human electrocardiograms, a very resource consuming task for physicians. In this paper we present a computational approach for arrhythmia detection based on heart rate variability signal analysis and the application of a neuro-fuzzy classification model called SONFIS. The aforementioned method generates a set of linguistically interpretable inference rules for pattern classification and outperforms artificial neural networks and support vector machines in accuracy and several other performance indicators.

Original languageEnglish
Title of host publicationProceedings - 29th International Conference of the Chilean Computer Science Society, SCCC 2010
PublisherIEEE Computer Society
Pages205-211
Number of pages7
ISBN (Print)9780769544007
DOIs
StatePublished - 2010
Externally publishedYes

Publication series

NameProceedings - International Conference of the Chilean Computer Science Society, SCCC
ISSN (Print)1522-4902

Keywords

  • Arrhythmia
  • Artificial Neural Networks
  • Fuzzy Logic
  • Heart Rate Variability
  • Support Vector Machines

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