## 4 Discussion  
It is generally accepted that entropy is an index used to measure the complexity of a system. Progress in understanding the difference between normal and fatigue period has been vigorous as a result of gains achieved by investigators employing the study of brain entropy. The indexes and the related classification performance adopted in previous studies are listed in Table 4. [PERSON] [37] used the approximate entropy and combined a variety of physiological signals for 20 subjects, by using the method of multi feature combined analysis method with a high success rate of 96.5%. [PERSON] [38] employed 31 subjects to participate in the study, combining EEG and EOG to analyze with the final classification accuracy of 95%. [PERSON] [39] used the sample entropy to study EEG signals. The results of reference [37; 38; 39] show that using various physiological features and a variety of entropy fusion method could obtain an obvious improvement for the classification accuracy. However, various physiological features could increase the difficulty of signal acquisition and result in a difficult comparison with different entropies for different signal sources. From the use of a single entropy feature, the results of this paper indicated that the combined entropy features could have better performance based on the EEG signal, which reduced the difficulty of signal collection. In short, compared with other existing EEG based driver fatigue analysis methods, the combined entropy feature analysis gives better classification result. For different analysis targets, using different entropies may have different impacts on the classification accuracy. In this paper we selected four types of entropies for detecting driver fatigue. Tables 1 and 4 indicate that, for the same data source, the classification parameters of the four entropies are notably different. In our experiment paradigm, the fuzzy entropy has the highest accuracy if single entropy is used as input, followed by sample entropy and approximate entropy, while the spectrum entropy records the lowest performance. The results are satisfactory: using the FuzzyEn and the SampleEn as features, the average accuracies are 93.50% and 89.75%, respectively. As we can see from Figure 2 and Table 4, using a FuzzyEn-based classification, the accuracy of the different data sets was different. The accuracy of the No. 8 dataset was 87.05%, and the average accuracy of the No. 2 dataset was as high as 96.75%. The accuracy was a little different, and the reason for this slight discrepancy might be that the No. 2 dataset was collected from T5 electrode, while the No. 8 dataset was collected from FP2 electrode. For the whole dataset, FuzzyEn-based classification greatly enhanced detection performance. Considering the ease of data acquisition, the present results implied that T5 electrode data with analysis with FuzzyEn-based classification also had very high detection performance and are suitable for research into the detection of driving fatigue. Figure 6: The classification accuracies with four different kernel functions. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Research Group** & **Number of Subjects** & **Feature Types** & **Classifier** & **Adopted Entropy** & _Acc_ \\ \hline Zhang [37] & 20 & EEG + EOG + EMG & neural network & Approximate & 96.50\% \\ \hline [PERSON] [38] & 31 & EEG + EOG &
\begin{tabular}{c} The Fuzzy Mutual Information based \\ Wavelet Packet Algorithm \\ \end{tabular} & Fuzzy & 95\% \\ \hline [PERSON] [39] & 28 & EEG & Threshold of ROC curve & Sample & 95\% \\ \hline This paper & 12 & EEG & SVM (support vector machine) & Combined Entropy & 98.75\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Studies regarding driver fatigue detection using different types of entropy. EOG: electrooculogram. In addition, the existing driving fatigue analysis results show that the frequency features of EEG signals have obviously changed when the drivers were in fatigue driving.