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| Emotional Stress Assessment by Combining Characters of Complexity and Entropy |
1 Institute of Biomedical Engineering, Yanshan University, Qinhuangdao 066004, China
2 Measurement Technology and Instrumentation Key Lab of Hebei Province，Qinhuangdao 066004, China
3 Institute of Information Science and Technology, Dalian Polytechnic University, Dalian116000, China |
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Abstract Pressure in long term may cause some diseases. It is important to assess the state of emotional stress reasonably and effectively. It is a reasonable method of pressure condition assessment based on the EEG (electroencephalograph), because of EEG contains plenty of emotional information. In this paper, focusing on the EEG signal characters extracting of emotional stress, an algorithm was investigated. The degree of random was quantified by the Kc factor. The complexity and energy distributing were quantfied by the approximate entropy and wavelet entropy. The Kc factor, approximate entropy and wavelet entropy were fused as the emotional characters by the optimal support vector machine. Based on principle of the overall optimization and the survival of the fittest of genetic algorithm, selectioncrossovermutation were done to pursue the optical parameters of SVM. “Fruit Ninja” game was selected as a source of stress, and a total of 92 groups of EEG signals were collected from 8 subjects. Assessment results showed that the highest classification accuracy was 94.12%, and average accuracy was 82.06%. The level of sensitivity to the stress was different among the brain regions. The left hemisphere was more sensitive to stress than the right one. The research is expected to be helpful for people to take proper methods of relieving stress and restoring physical and mental health.
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［1］Milette K, Roseman M, Thombs BD. Transparency of outcome reporting and trial registration of randomized controlled trials in top psychosomatic and behavioral health journals: A systematic review Original Research Article ［J］. Journal of Psychosomatic Research, 2011, 70 (3):205-217.
［2］Healey JA, Picard RW. Detecting stress during realworld driving tasks using physiological sensors ［J］.IEEE Intelligent Transportation Systems Society， 2005, 6(2):156-166.［3］Mokhayeri F, Akbarzadeh TMR. Mental stress detection based on soft computing techniques［C］// IEEE International Conference on Bioinformatics and Biomedicine. Atlanta:IEEE, 2011: 430- 433.
［4］Wagner J. Augsburg Biosignal Toolbox (AuBT) User Guide ［OL］. http://emotionresearch.net,2012-10-8/2012-12-5.
［5］Gaggioli AG, Pioggia G, Tartarisco G, et al. A system for automatic detection of momentary stress in naturalistic settings ［C］// Wiederhold BK, Riva G , eds. Studies in Health Technology & Informatics. Biussels: IOS Press, 2012: 182-186.
［6］Hernandez J, Morris RR, Picard RW. Call center stress recognition with personspecific models ［C］ // Mello SD, Graesser A,eds.Affective Computing and Intelligent Interaction, Berlin: SpringerVerlag, 2011, 125-134.
［7］刘秉正，彭建华．非线性动力学［M］．北京，高等教育出版社2004.
［8］LempelA，Ziv J．On the complexity of Finite sequence ［J］．IEEE TransInf Thcory,1976，IT22：75-81．
［9］Pincus SM. Approximate entropy as a measure of system complexity［J］. Proceedings of the National Academy of Science. 1991，88(6)：2297-2301.
［10］Bruhn J, Hoeift A, Ropcke H, et al. Approximate entropy as an electroencephalographic measure of anesthetic drug effect during desflurane anesthesia ［J］. Anesthesiology, 2000, 92(3):715-726.
［11］华光，任维，刘钢，等.近似熵及其在心率变异分析中的应用［J］．航天医学与医学工程，2000，13(6)：417-421．
［12］Ocak H．Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy ［J］. Export Systems with Applications, 2009, 〖STHZ〗36(2)：2027-2036．
［13］李建勋，柯熙政，丁德强. 一种基于小波熵的时间尺度算法［J］.天文学报,2007,48(1):84-92.
［14］Rosso OA, Blanco S, Yordanova J, et al. Wavelet entropy: a new tool for analysis of short duration brain electrical signals ［J］. Neurosci Meth, 2001,105(1): 65-75.
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