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

研究成果: Conference contribution

1 引文 斯高帕斯(Scopus)

摘要

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.

原文English
主出版物標題Advanced Intelligent Computing - 7th International Conference, ICIC 2011, Revised Selected Papers
頁面532-537
頁數6
DOIs
出版狀態Published - 2011 十二月 1
事件7th International Conference on Intelligent Computing, ICIC 2011 - Zhengzhou, China
持續時間: 2011 八月 112011 八月 14

出版系列

名字Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
6838 LNCS
ISSN(列印)0302-9743
ISSN(電子)1611-3349

Other

Other7th International Conference on Intelligent Computing, ICIC 2011
國家China
城市Zhengzhou
期間11-08-1111-08-14

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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