基于样本熵的实时心电图疲劳驾驶检测

Wang Fuwang, Wang Hong, Fu Rongrong

School of Mechanic Engineering, Northeast Electric Power University, Jilin 132012, China.

School of Mechanic Engineering, Northeastern University, Shenyang 110819, China.

Entropy (Basel). 2018 Mar 15;20(3):196. doi: 10.3390/e20030196.

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.

在当前工作中，通过样本熵（SampEn）计算得到的心率变异性（HRV）特征被用于分析连续驾驶阶段的驾驶疲劳状态。结合相对功率谱比β/（θ + α）、主观问卷以及脑电图（EEG）信号的脑网络参数，探讨了驾驶疲劳不同特征之间的关系。因此，可以得出结论，HRV特征（RR SampEn和R峰SampEn），以及通道（C3、C4、P3、P4）的相对功率谱比β/（θ + α）、主观问卷和脑网络参数，能够有效检测不同驾驶阶段的驾驶疲劳。此外，从手掌部位采集心电图信号的方法无需贴片电极，方便易行，未来在实际驾驶场景中具有实用价值。