Automatic classification of heartbeats

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

4 Scopus citations

Abstract

We report improvement in the detection of a class of heart arrhythmias based on electrocardiogram signals (ECG). The detection is performed using a 4 dimensional feature vector obtained by applying an iterative feature selection method used in conjunction with artificial neural networks. The feature set includes the pre-RR interval, which is a primary measure that cardiologists use in a clinical setting. A transformation applied to the pre-RR interval reduced the false positive rate. Our solution as opposed to existing literature does not rely on high-dimensional features such as wavelets, signal amplitudes which do not have direct relationship to heart function and difficult to interpret. Also we avoid obtaining patient specific labeled recordings. Furthermore, we propose semi-parametric classifiers as opposed to restrictive parametric linear discriminant analysis and its variants, which are a mainstay in ECG classification. Extensive experiments from the MIT-BIH databases demonstrate superior performance by our methods.

Original languageEnglish
Title of host publication2014 Proceedings of the 22nd European Signal Processing Conference, EUSIPCO 2014
Pages1542-1546
Number of pages5
ISBN (Electronic)9780992862619
StatePublished - 10 Nov 2014
Event22nd European Signal Processing Conference, EUSIPCO 2014 - Lisbon, Portugal
Duration: 1 Sep 20145 Sep 2014

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491

Conference

Conference22nd European Signal Processing Conference, EUSIPCO 2014
Country/TerritoryPortugal
CityLisbon
Period1/09/145/09/14

Keywords

  • Artificial neural networks
  • Classification
  • Discriminant analysis
  • ECG
  • False positives

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