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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/21/10279 |
| _version_ | 1797512787214204928 |
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| author | Tao Zhang Jichi Chen Enqiu He Hong Wang |
| author_facet | Tao Zhang Jichi Chen Enqiu He Hong Wang |
| author_sort | Tao Zhang |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T06:06:37Z |
| format | Article |
| id | doaj.art-7feb3d8ba61c499e816d6696738eb350 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T06:06:37Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-7feb3d8ba61c499e816d6696738eb3502023-11-22T20:30:43ZengMDPI AGApplied Sciences2076-34172021-11-0111211027910.3390/app112110279Sample-Entropy-Based Method for Real Driving Fatigue Detection with Multichannel ElectroencephalogramTao Zhang0Jichi Chen1Enqiu He2Hong Wang3College of Applied Technology, Shenyang University, Shenyang 110044, ChinaSchool of Mechanical Engineering, Shenyang University of Technology, Shenyang 110870, ChinaSchool of Chemical Equipment, Shenyang University of Technology, Liaoyang 111003, ChinaDepartment of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, ChinaSafe 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.https://www.mdpi.com/2076-3417/11/21/10279EEGreal drivingfatigue |
| spellingShingle | Tao Zhang Jichi Chen Enqiu He Hong Wang Sample-Entropy-Based Method for Real Driving Fatigue Detection with Multichannel Electroencephalogram Applied Sciences EEG real driving fatigue |
| title | 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_short | 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 |
| topic | EEG real driving fatigue |
| url | https://www.mdpi.com/2076-3417/11/21/10279 |
| work_keys_str_mv | AT taozhang sampleentropybasedmethodforrealdrivingfatiguedetectionwithmultichannelelectroencephalogram AT jichichen sampleentropybasedmethodforrealdrivingfatiguedetectionwithmultichannelelectroencephalogram AT enqiuhe sampleentropybasedmethodforrealdrivingfatiguedetectionwithmultichannelelectroencephalogram AT hongwang sampleentropybasedmethodforrealdrivingfatiguedetectionwithmultichannelelectroencephalogram |