Intelligent arrhythmia detection and classification using ICA

Asad Azemi, Vahid R. Sabzevari, Morteza Khademi, Hossein Gholizade, Arman Kiani, Zeinab S. Dastgheib

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

13 Scopus citations

Abstract

In this paper a novel approach for cardiac arrhythmias detection is proposed. The proposed method is based on using Independent Component Analysis (ICA) and wavelet transform to extract important features. Using the extracted features different machine learning classification schemas, MLP and RBF neural networks and K- nearest neighbor, are used to classify 274 instance signals from the MIT-BIH database. Simulations show that multilayer neural networks with Levenberg-Marquardt (LM) back propagation algorithm provide the optimal learning system. We were able to obtain 98.5% accuracy, which is an improvement in comparison with the similar works.

Original languageEnglish (US)
Title of host publication28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06
Pages2163-2166
Number of pages4
DOIs
StatePublished - Dec 1 2006
Event28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06 - New York, NY, United States
Duration: Aug 30 2006Sep 3 2006

Publication series

NameAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
ISSN (Print)0589-1019

Other

Other28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06
CountryUnited States
CityNew York, NY
Period8/30/069/3/06

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

Fingerprint Dive into the research topics of 'Intelligent arrhythmia detection and classification using ICA'. Together they form a unique fingerprint.

Cite this