Electrocardiogram for biometrics by using adaptive multilayer generalized learning vector quantization (AMGLVQ): Integrating feature extraction and classification

Elly Matul Imah, Wisnu Jatmiko, T. Basaruddin

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)

Abstract

Electrocardiogram (ECG) signal for human identity recognition is a new area on biometrics research. The ECG is a vital signal of human body, unique, robustness to attack, universality and permanence, difference to others traditional biometrics technic. This study also proposes Adaptive Multilayer Generalized Learning Vector Quantization (AMGLVQ), that integrating feature extraction and classification method. The experiments shown that AMGLVQ can improve the accuracy of classification better than SVM or back-propagation NN and also able to handle some problems of heartbeat classification: imbalanced data set, inconsistency between feature extraction and classification and detecting unknown data on testing phase.

Original languageEnglish
Pages (from-to)1891-1917
Number of pages27
JournalInternational Journal on Smart Sensing and Intelligent Systems
Volume6
Issue number5
Publication statusPublished - 1 Jan 2013

Keywords

  • AMGLVQ
  • Back-propagation-NN
  • Classification
  • ECG biometrics
  • Feature extraction
  • SVM
  • Vector quantization

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