Ventricular ectopic beats classification using Sparse Representation and Gini Index

Hamza Baali, Mostefa Mesbah

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

6 Citations (Scopus)

Abstract

In this study, we consider using sparse representation and the Gini Index (GI) for Arrhythmia classification. Our approach involves, first, designing a separate dictionary for each Arrhythmia class using a set of labeled training QRS complexes. Sparse representations, based on the designed dictionaries, of each new test QRS complex are then calculated. Its class is finally predicted using the winner-takes-all principle; that is, the class associated with the highest GI is chosen. Our experiments showed promising results for the classification of premature ventricular contractions using a patient-specific approach. For many of the subjects considered, our classifier attained accuracies close to 100 % on the test set.

Original languageEnglish
Title of host publication2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5821-5824
Number of pages4
ISBN (Electronic)9781424492718
DOIs
Publication statusPublished - Nov 4 2015
Event37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015 - Milan, Italy
Duration: Aug 25 2015Aug 29 2015

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Volume2015-November
ISSN (Print)1557-170X

Other

Other37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
Country/TerritoryItaly
CityMilan
Period8/25/158/29/15

ASJC Scopus subject areas

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

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