Heart arrhythmia detection using continuous wavelet transform and principal component analysis with neural network classifier

Parham Ghorbanian, Ali Ghaffari, Ali Jalali, C. Nataraj

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

Abstract

The aim of this study is to develop an algorithm to detect and classify six types of electrocardiogram (ECG) signal beats including normal beats (N), atrial premature beats (A), right bundle branch block beats (R), left bundle branch block beats (L), paced beats (P), and premature ventricular contraction beats (PVC or V) using a neural network classifier. In order to prepare an appropriate input vector for the neural classifier several preprocessing stages have been applied. Continuous wavelet transform (CWT) has been applied in order to extract features from the ECG signal. Moreover, Principal component analysis (PCA) is used to reduce the size of the data. Finally, the MIT-BIH database is used to evaluate the proposed algorithm, resulting in 99.5% sensitivity (Se), 99.66% positive predictive accuracy (PPA) and 99.17% total accuracy (TA).

Original languageEnglish (US)
Title of host publicationComputing in Cardiology 2010, CinC 2010
Pages669-672
Number of pages4
StatePublished - 2010
Externally publishedYes
EventComputing in Cardiology 2010, CinC 2010 - Belfast, United Kingdom
Duration: Sep 26 2010Sep 29 2010

Publication series

NameComputing in Cardiology
Volume37
ISSN (Print)2325-8861
ISSN (Electronic)2325-887X

Conference

ConferenceComputing in Cardiology 2010, CinC 2010
CountryUnited Kingdom
CityBelfast
Period9/26/109/29/10

ASJC Scopus subject areas

  • Computer Science(all)
  • Cardiology and Cardiovascular Medicine

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