Background

Fatigue is one of the important factors in traffic accidents. Hence, it is necessary to devise methods to detect the fatigue and apply practical fatigue detection solutions for drivers.

New Method

This paper presents a method based on the empirical mode decomposition(EMD) of multi-scale entropy on the recorded forehead Electroencephalogram(EEG) signals. These EEG signals are decomposed to extract intrinsic mode functions(IMFs) by using the EMD technique. Then, the IMFs components are selected out by using the Pearson correlation coefficient and the best scale features on each signal are determined in multiple experiments.

Results

Results indicate that the empirical mode decomposition multi-scale fuzzy entropy feature classification recognition rate is up to 87.50%, the highest is 88.74%, which is 23.88% higher than the single-scale fuzzy entropy and 5.56% higher than multi-scale fuzzy entropy.

Comparison with Existing Method

Three types of entropies measures, permutation entropy(PE), sample entropy(SE), fuzzy entropy(FE), were applied for the analysis of signal and compared by seven classifiers in 10-fold and Leave-One-Out cross-validation experiments.

Conclusions

The proposed method can be effectively applied to the detection of driving fatigue.

中文翻译：

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基于经验模态分解（EMD）的多尺度熵的构造及其在驾驶疲劳识别中的应用。

背景

疲劳是交通事故的重要因素之一。因此，有必要设计一种方法来检测疲劳并为驾驶员应用实用的疲劳检测解决方案。

新方法

本文提出了一种基于对所记录的额头脑电图（EEG）信号进行多尺度熵经验模态分解（EMD）的方法。这些EEG信号通过使用EMD技术进行分解，以提取本征模式函数（IMF）。然后，使用皮尔逊相关系数选择IMF分量，并通过多次实验确定每个信号的最佳比例特征。

结果

结果表明，经验模态分解的多尺度模糊熵特征分类识别率高达87.50％，最高为88.74％，比单尺度模糊熵高23.88％，比多尺度模糊熵高5.56％。 。

与现有方法的比较

将三种熵测度：置换熵（PE），样本熵（SE），模糊熵（FE）用于信号分析，并通过七个分类器在十倍和留一法交叉验证实验中进行比较。

结论

所提出的方法可以有效地应用于驾驶疲劳的检测。