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Open AccessArticle

Real-Time ECG-Based Detection of Fatigue Driving Using Sample Entropy

1
School of Mechanic Engineering, Northeast Electric Power University, Jilin 132012, China
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School of Mechanic Engineering, Northeastern University, Shenyang 110819, China
3
College of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
*
Author to whom correspondence should be addressed.
Entropy 2018, 20(3), 196; https://doi.org/10.3390/e20030196
Received: 3 February 2018 / Revised: 3 March 2018 / Accepted: 13 March 2018 / Published: 15 March 2018
In present work, the heart rate variability (HRV) characteristics, calculated by sample entropy (SampEn), were used to analyze the driving fatigue state at successive driving stages. Combined with the relative power spectrum ratio β/(θ + α), subjective questionnaire, and brain network parameters of electroencephalogram (EEG) signals, the relationships between the different characteristics for driving fatigue were discussed. Thus, it can conclude that the HRV characteristics (RR SampEn and R peaks SampEn), as well as the relative power spectrum ratio β/(θ + α) of the channels (C3, C4, P3, P4), the subjective questionnaire, and the brain network parameters, can effectively detect driving fatigue at various driving stages. In addition, the method for collecting ECG signals from the palm part does not need patch electrodes, is convenient, and will be practical to use in actual driving situations in the future. View Full-Text
Keywords: HRV; sample entropy; driving fatigue; relative power spectrum ratio β/(θ + α); brain networks HRV; sample entropy; driving fatigue; relative power spectrum ratio β/(θ + α); brain networks
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Wang, F.; Wang, H.; Fu, R. Real-Time ECG-Based Detection of Fatigue Driving Using Sample Entropy. Entropy 2018, 20, 196.

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