TY - GEN
T1 - Using heart rate variability parameter-based feature transformation algorithm for driving stress recognition
AU - Wang, Jeen-Shing
AU - Lin, Che-Wei
AU - Yang, Ya-Ting Carolyn
PY - 2011/12/1
Y1 - 2011/12/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84863173032&partnerID=8YFLogxK
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U2 - 10.1007/978-3-642-24728-6_72
DO - 10.1007/978-3-642-24728-6_72
M3 - Conference contribution
AN - SCOPUS:84863173032
SN - 9783642247279
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 532
EP - 537
BT - Advanced Intelligent Computing - 7th International Conference, ICIC 2011, Revised Selected Papers
T2 - 7th International Conference on Intelligent Computing, ICIC 2011
Y2 - 11 August 2011 through 14 August 2011
ER -