Why the study?
Does ECG-based heart rate variability calculated by sample entropy effectively detect driving fatigue?
Does ECG-based heart rate variability calculated by sample entropy effectively detect driving fatigue?
ECG-based HRV characteristics using sample entropy, collected conveniently from the palm without patch electrodes, can effectively detect driving fatigue.
May support palm-based HRV monitoring for driver fatigue; leaves open prospective validation before clinical adoption.
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.
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Wang et al. (2018) studied this question.
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