Sample-Entropy-Based Method for Real Driving Fatigue Detection with Multichannel Electroencephalogram

Safe driving plays a crucial role in public health, and driver fatigue causes a large proportion of crashes in road driving. Hence, this paper presents the development of an efficient system to determine whether a driver is fatigued during real driving based on 14-channel EEG signals. The complexity...

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Autores principales:	Tao Zhang, Jichi Chen, Enqiu He, Hong Wang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	EEG real driving fatigue Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/7feb3d8ba61c499e816d6696738eb350
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spelling	oai:doaj.org-article:7feb3d8ba61c499e816d6696738eb3502021-11-11T15:19:41ZSample-Entropy-Based Method for Real Driving Fatigue Detection with Multichannel Electroencephalogram10.3390/app1121102792076-3417https://doaj.org/article/7feb3d8ba61c499e816d6696738eb3502021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/10279https://doaj.org/toc/2076-3417Safe driving plays a crucial role in public health, and driver fatigue causes a large proportion of crashes in road driving. Hence, this paper presents the development of an efficient system to determine whether a driver is fatigued during real driving based on 14-channel EEG signals. The complexity of the EEG signal is then quantified with the sample entropy method. Finally, we explore the performance of multiple kernel-based algorithms based on sample entropy features for classifying fatigue and normal subjects by only analyzing noninvasive scalp EEG signals. Experimental results show that the highest classification accuracy of 97.2%, a sensitivity of 95.6%, a specificity of 98.9%, a precision of 98.9%, and the highest AUC value of 1 are achieved using SampEn feature and cubic SVM classifier (SCS Model). It is hence concluded that SampEn is an effectively distinguishing feature for classifying normal and fatigue EEG signals. The proposed system may provide us with a new and promising approach to monitoring and detecting driver fatigue at a relatively low computational cost.Tao ZhangJichi ChenEnqiu HeHong WangMDPI AGarticleEEGreal drivingfatigueTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10279, p 10279 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	EEG real driving fatigue Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	EEG real driving fatigue Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Tao Zhang Jichi Chen Enqiu He Hong Wang Sample-Entropy-Based Method for Real Driving Fatigue Detection with Multichannel Electroencephalogram
description	Safe driving plays a crucial role in public health, and driver fatigue causes a large proportion of crashes in road driving. Hence, this paper presents the development of an efficient system to determine whether a driver is fatigued during real driving based on 14-channel EEG signals. The complexity of the EEG signal is then quantified with the sample entropy method. Finally, we explore the performance of multiple kernel-based algorithms based on sample entropy features for classifying fatigue and normal subjects by only analyzing noninvasive scalp EEG signals. Experimental results show that the highest classification accuracy of 97.2%, a sensitivity of 95.6%, a specificity of 98.9%, a precision of 98.9%, and the highest AUC value of 1 are achieved using SampEn feature and cubic SVM classifier (SCS Model). It is hence concluded that SampEn is an effectively distinguishing feature for classifying normal and fatigue EEG signals. The proposed system may provide us with a new and promising approach to monitoring and detecting driver fatigue at a relatively low computational cost.
format	article
author	Tao Zhang Jichi Chen Enqiu He Hong Wang
author_facet	Tao Zhang Jichi Chen Enqiu He Hong Wang
author_sort	Tao Zhang
title	Sample-Entropy-Based Method for Real Driving Fatigue Detection with Multichannel Electroencephalogram
title_short	Sample-Entropy-Based Method for Real Driving Fatigue Detection with Multichannel Electroencephalogram
title_full	Sample-Entropy-Based Method for Real Driving Fatigue Detection with Multichannel Electroencephalogram
title_fullStr	Sample-Entropy-Based Method for Real Driving Fatigue Detection with Multichannel Electroencephalogram
title_full_unstemmed	Sample-Entropy-Based Method for Real Driving Fatigue Detection with Multichannel Electroencephalogram
title_sort	sample-entropy-based method for real driving fatigue detection with multichannel electroencephalogram
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/7feb3d8ba61c499e816d6696738eb350
work_keys_str_mv	AT taozhang sampleentropybasedmethodforrealdrivingfatiguedetectionwithmultichannelelectroencephalogram AT jichichen sampleentropybasedmethodforrealdrivingfatiguedetectionwithmultichannelelectroencephalogram AT enqiuhe sampleentropybasedmethodforrealdrivingfatiguedetectionwithmultichannelelectroencephalogram AT hongwang sampleentropybasedmethodforrealdrivingfatiguedetectionwithmultichannelelectroencephalogram
_version_	1718435374165917696

Sample-Entropy-Based Method for Real Driving Fatigue Detection with Multichannel Electroencephalogram

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