A Prediction Model for Traffic Accident Proneness Caused by Drunk Driving via Numerical Characteristics of Drivers’ EEGs

Abstract

Abstract:

Abstract： This paper introduces the establishment of a predicting model for normalized traffic accident proneness (NAP) after drinking. Eighteen drivers’ EEGs and traffic accident proneness were measured respectively in different drunken states. Considering the instant complexity and longterm periodicity of EEGs measured from the left frontal lobe, the power gain of δ wave and fuzzy entropy of EEG were invented and calculated. A hybrid SigmaPi neural network was introduced and studied to help building the predict model for NAP from the aspects of both power gain of δ wave and fuzzy entropy of EEG. Experiments proved that the predicted values of NAP accord with the actual values, with a consistent increase and decrease characteristic. When the amount of the alcohol drunk is less than 50% of the subjective maximum alcohol to drink, the errors were very small, but when the amount of the alcohol drunk is more than 50%, the errors became bigger along with the increase volume of alcohol to drink.

Key words: drunk driving, traffic accident proneness, electroencephalogram (EEG), power gain of &delta, wave, fuzzy entropy

CLC Number:

U 491.2

ZHONG Mingen1,WU Pingdong2，PENG Junqiang3,HONG Hanchi1. A Prediction Model for Traffic Accident Proneness Caused by Drunk Driving via Numerical Characteristics of Drivers’ EEGs[J]. Journal of Shanghai Jiaotong University, 2015, 49(02): 287-292.

References

［1］World Health Organization. Global status report on road safety: Time for action［M］. Geneva: WHO Press, 2009.

［2］World Health Organization. Global status report on road safety: Supporting a decade of action［M］. Geneva: WHO Press. 2013.

［3］顾雯雯,俞敏,丛黎明. 酒后驾车现状与危害及预防控制研究进展［J］. 中国预防医学杂志, 2010, 11(08):854857.

GU Wenwen, YU Min, CHONG Liming. Review of present situation, harm and prevention control about drunk driving［J］. China Preventive Medicine, 2010, 11(08):854857.

［4］钟铭恩, 吴平东, 彭军强, 等．基于血液酒精浓度和脑电特征的酒后驾车事故倾向对比研究［J］. 中国生物医学工程学报, 2013,32(3):378383.

ZHONG Mingen, WU Pingdong, PENG Junqiang, et al. Comparative study of traffic accident proneness for drunk driving via blood alcohol concentration and EEG［J］. Chinese Journal of Biomedical Engineering, 2013, 32(3):378383.

［5］Murata K, Fujita E, Kojima S, et al. Noninvasive biological sensor system for detection of drunk driving［J］. IEEE Transactions on Information Technology in Biomedicine, 2011, 15(1):1925.

［6］Sakairi M, Suzuki D, Nishimura A, et al. Simultaneous detection of breath and alcohol using breathalcohol sensor for prevention of drunk driving［J］. IEICE Electronics Express, 2010, 7(6):467472.

［7］Koukiou G, Panagopoulos G, Anastassopoulos V. Drunk person identification using thermal infrared images［C］∥Processing of the 16th International Conference on Digital Signal. Santorini, Greecea: IEEE Press, 2009: 46.

［8］Bear M F, Connors B W, Paradiso M A. Neuroscience: Exploring the Brain 3rd edition［M］. USA: Lippincott Williams & Wilkins Press, 2007.

［9］Papadelis S C, Chen Z, Papadeli C K. Monitoring sleepiness with onboard electrophysiological recordings for preventing sleepdeprived traffic accidents［J］. Clinical Neurophysiology, 2007, 118(09):19061922．

［10］邹阳, 苗夺谦, 王登. 醉酒者和正常人脑电的样本熵研究［J］. 中国生物医学工程学报, 2010, 29(06): 939942.

ZOU Yang, MIAO Duoqian, WANG Deng. Research on sample entropy of alcoholic and normal people［J］. Chinese Journal of Biomedical Engineering, 2010, 29(06): 939942.

［11］范金锋, 邵晨曦, 王剑, 等. 醉酒者脑电和正常脑电非线性特性的比较评估［J］. 中国生物医学工程学报, 2008, 27(01):1828.

FAN Jinfeng, SHAO Chenxi, WANG Jian, et al. A comparative evaluation of nonlinear characteristics of EEG for alcoholic and normal people［J］. Chinese Journal of Biomedical Engineering, 2008, 27(01):1828.

［12］Wu D, Chen Z H, Feng R F, et al. Study on human brain after consuming alcohol based on EEG signal［C］∥Proceedings of the 3rd International Conference on Computer Science and Information Technology. Bangalore India: IEEE Press, 2010: 406409.

［13］Yazdani A, Setarehdan S K. Classification of EEG signals correlated with alcohol abusers［C］∥Proceedings of the 9th International Symposium on Signal Processing and Its Application. Sharjah: IEEE Press, 2007: 14.

［14］Kousarrizi M R N, Ghanbari A A, Gharaviri A, et al. Classification of alcoholics and nonalcoholics via EEG using SVM and neural networks［C］∥Proceedings of the 3rd International Conference on Bioinformatics and Biomedical Engineering. Beijing: IEEE Press, 2009: 14.

［15］钟铭恩, 方遒, 洪汉池, 等. 驾驶员脑电模糊熵与酒后驾车交通事故倾向相关性研究［J］. 中国安全科学学报, 2013,23(8):5964.

ZHONG Mingen, FANG Qiu, HONG Hanchi. A correlation study between fuzzy entropy of drivers’EEGs and traffic accident proneness for drunk driving［J］. China Safety Science Journal, 2013,23(8):5964.

［16］李纯明, 陈天平. SigmaPi 神经网络中的逼近问题［J］. 科学通报, 1996, 41(8): 683684.

LI Chunming, CHEN Tianping. Approximation problem in SigmaPi neural network［J］. Chinese Science Bulletin, 1996, 41(8): 683684.

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