## 3 Experiment and Results
### Classification Result
Furthermore, Table 2 shows that classification accuracies of each subject with respect to single entropy and combined entropy. In order to analysis the results with a statistical significance, we calculated the \(p\) value of the \(t\) test. Using \(p1\) as the \(t\) test with the SpectralEn and CombinedEn, \(p2\) as ApproxEn and CombinedEn, \(p3\) as SampleEn and CombinedEn, and \(p4\) as FuzzyEn and CombinedEn, \((p1,p2,p3,p4)=(1.7,90,1.6,20)\times 10^{-5}\) was obtained, from which we know that an obvious difference \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Entropies** & _Acc_ & _Sp_ & _Sn_ & _MCC_ \\ \hline SpectralEn & 75.00 & 78.00 & 72.00 & 47.08 \\ ApproxEn & 87.25 & 84.05 & 87.50 & 73.57 \\ SampleEn & 89.75 & 84.50 & 91.00 & 78.66 \\ FuzzyEn & 93.50 & 92.50 & 91.50 & 85.02 \\ CombinedEn & 98.75 & 97.50 & 96.00 & 93.51 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The classification results. _Acc_: accuracy; _Sn_: sensitivity; _Sp_: specificity; _MCC_: Mathew’s correlation coefficient  
Figure 5: The value of mean and variance in two states with respect to the combined entropy. between single entropy as features and combined entropy as features is shown. Table 3 also shows that the average success rates based on combined entropy had better performance than that using single entropy. In addition, approximate entropy and sample entropy have a similar degree of detection of EEG signals of fatigue and normal state because of the \(p\) value of the \(t\) test is 0.01, implying that there is little difference. When single entropy is used for classification, fuzzy entropy gives the highest average discrimination rate at 93.50%. Approximate entropy and sample entropy come next with scores around 88%, and spectrum entropy comes up with the lowest accuracy with 75%. Furthermore, as the top performer, fuzzy entropy's three parameter averages are, 91.50%, 92.50% and 85.02%, respectively. Next in order is sample entropy, whose three parameter average are 91.00%, 84.50% and 78.66%, respectively. These results indicate that, in the case of driver fatigue detection, fuzzy entropy and sample entropy are more effective and stable EEG features for classification. It is well-known that the feature distance and the difference significance as class features vary from electrode to electrode. For EEG analysis, therefore, it is necessary to conduct backtracking to single out those electrodes most sensitive to driver fatigue, based on the signal feature. In order to demonstrate the difference significance in various electrodes for the 300 samples of each type, the \(t\) test is performed for the two types with data from a certain electrode. The \(p\) value of the \(t\) test is listed in descending order, and for the \(n\) subjects, the top four electrodes are shown in Table 3, which displays the mean values of entropy for the subjects and the related test results during normal state and fatigue state, respectively. The row labeled by variation in Table 3 represents the variation of the fatigue state compared with normal state; specifically, \(\uparrow\) denotes an increase of entropy value in fatigue state, while \(\downarrow\) denotes a reduction of entropy value in fatigue state. As known to all, the kernel function can affect the classification performance of the SVM classifier. In order to study the different influence on classification results, we used the linear function, the polynomial function, the radial basis function and the sigmoid function as the kernel function, respectively. Shown as Figure 6, the comparison of the average accuracies for 12 subjects with four different kernel functions was made; its average accuracies are 98.5%, 98.3%, 98.7% and 97.1%, respectively, which indicated that classification performance using the radial basis function as kernel function obtained a better result.