Frequency Rank Order Statistic with Unknown Neural Network for ECG Identification System

Kuo Kun Tseng, Dachao Lee, William Hurst, Fang Yin Lin, W.H. Ip

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

2 Citations (Scopus)

Abstract

Electrocardiograms (ECG) contain biological information which is unique to the individual. In this paper, an ECG identification system, which uses Frequency Rank Order Statistics (FROS) as a feature extraction method and Back-Propagation Neural Network (BPNN) classifiers to identify 'other classes', is proposed. FROS handle different ECG states and BPNN classifiers, with random input weights, are used to generate a relatively high accuracy model for the identification system. Additionally, in the output layer, classified patterns are categorized according to the maximum value of the output layer nodes. Similar data is grouped into one category for the final identification result. Experiments show that the BPNN classifier produces more accurate results than an SVM and Bayesian classifier achieve on average. The proposed approach also out-performs SVMNN and LVQNN. The identification system, put forward in this paper, may be applied to an intelligent vehicular system, as an application example.

Original languageEnglish
Title of host publicationProceedings - 4th International Conference on Enterprise Systems
Subtitle of host publicationAdvances in Enterprise Systems, ES 2016
EditorsGang Li, Yale Yu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages160-167
Number of pages8
ISBN (Electronic)9780769559841
DOIs
Publication statusPublished - 16 Mar 2017
Externally publishedYes
Event4th International Conference on Enterprise Systems, ES 2016 - Melbourne, Australia
Duration: 2 Nov 20163 Nov 2016

Publication series

NameProceedings - 4th International Conference on Enterprise Systems: Advances in Enterprise Systems, ES 2016

Conference

Conference4th International Conference on Enterprise Systems, ES 2016
CountryAustralia
CityMelbourne
Period2/11/163/11/16

Keywords

  • Biometric
  • ECG
  • Neural Network
  • Unknown individual recognition

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