Using heart rate variability parameter-based feature transformation algorithm for driving stress recognition

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

1 Citation (Scopus)

Abstract

This paper presents a heart rate variability (HRV) parameter-based feature transformation algorithm for driving stress recognition. The proposed parameter-based transformation algorithm consists of feature generation, feature selection, and feature dimension reduction. In order to generate significant features from ECG signals, parameter-based feature generation method is proposed in this study. The parameter-based method calculates features from five-minute HRV analysis. The kernel-based class separability (KBCS) is employed as the selection criterion for feature selection. To reduce computational load of the algorithm, principal component analysis (PCA) and linear discriminant analysis (LDA) are adopted for feature dimension reduction. Our experimental results show that the combination of KBCS, LDA, and PCA can achieve satisfactory recognition rates for the features generated by parameter-based feature generation method. The main contribution of this study is that our proposed approach can use only ECG signals to effectively recognize driving stress conditions with very good recognition performance.

Original languageEnglish
Title of host publicationAdvanced Intelligent Computing - 7th International Conference, ICIC 2011, Revised Selected Papers
Pages532-537
Number of pages6
DOIs
Publication statusPublished - 2011 Dec 1
Event7th International Conference on Intelligent Computing, ICIC 2011 - Zhengzhou, China
Duration: 2011 Aug 112011 Aug 14

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6838 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other7th International Conference on Intelligent Computing, ICIC 2011
CountryChina
CityZhengzhou
Period11-08-1111-08-14

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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