## 4 Discussion
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Reference** & **Signals** & **Features** & **Techniques Used** & **Datasets Required for Training** & **Accuracy (\%)** \\ \hline \multirow{4}{*}{This work} & \multirow{4}{*}{ECG} & Single features: & Heartbeat mode analysis and k-NN & Single driving dataset & 2 classes: 93.7\% \\  & & & & & driving & 3 classes: 81.7\% \\  & & & _bmNN_ Vs. _avNN_ & & & dataset & _avNN_: \\  & & & & & & 2 classes: 76.1\% \\  & & & & & & 3 classes: 59.1\% \\ \hline \multirow{4}{*}{[PERSON] et al., 2021 [6]} & \multirow{4}{*}{ECG} & Multiple features: & & & & 2 classes \\  & & _avNN_, & & & & (Recall) \\  & & RMSSD, & & and GB & & SVM: 79\% \\  & & TF ULE, & & & & MLP: 81\% \\  & & SDNN & & & & RF: 81\% \\  & & & & & GB: 80\% \\ \hline \multirow{4}{*}{[PERSON] et al., 2020 [5]} & ECG, EMG, & Multiple features: & & IPCA, CBC, KMC, and the & & 3 classes \\  & & GSR, RESP & & automatic Selection & k-NN-Weighted classifier & All driving datasets & ECG: 75.02\% \\ \cline{1-1} \cline{2-5}  & & & & classifier & & GSR: 72.05\% \\ \hline \multirow{4}{*}{[PERSON] et al., 2018 [23]} & \multirow{4}{*}{GSR} & Multiple features: & Fisher projection and LDA & & & \\  & & & 18 features & & & \\ \cline{1-1} \cline{2-5}  & [PERSON] et al., 2017 [21] & ECG, EMG, & Multiple features: & & SBL, PCA, SVM, and ELM & All driving datasets & 3 classes: 99\% \\ \cline{1-1} \cline{2-5}  & & & & & & \\ \cline{1-1} \cline{2-5}  &  
In the study by [PERSON] et al. (2019), GSR had the best real-time correlation with stress, followed by heart rate and HRV frequency domain indicators. We also discovered that the different driving sections of Drive 02\(\backslash\)04\(\backslash\)06\(\backslash\)08\(\backslash\)11\(\backslash\)13 had average _avNN_ values in the order of Rest > Hwy > City. That is, stress was correlated with heart rate such that the latter increased with the former, which is consistent with existing research results ([PERSON] et al., 2019; [PERSON] et al., 2019). However, the change in the average _avNN_ value was not significant, and differences existed among individuals. For the six driving datasets classified with the _avNN_ feature, the average classification accuracy was 76.1%, whereas the classification accuracy for Drive08 was only 26.8%. For Drive08, the average values of _avNN_ were Rest (0.987) > Hwy (0.969) > City (0.914), presented in Table 3, and the similar values of _avNN_ in the different driving sections were the reason for the extremely low classification accuracy. Meanwhile, the identification accuracy for Drive07\(\backslash\)15 data classified into the 0 category was good, indicating that the _avNN_ feature value did not exhibit any changes to the GSR signal during the rest periods. After comparing it with the _avNN_ feature value, the value of _bmNN_ was correlated to the GSR signals, which reflected the emotional changes in real time. Additionally, owing to individual differences, the _avNN_ values for some driving datasets did not change significantly when the stress changed. For _bmNN_, different values for \(m\) and _bm_ were used to, through trial and error, find the relevant beating mode to the individual stress to achieve an individualized analysis. The average _bmNN_ feature values for the three driving sections of the five driving datasets (Drive02\(\backslash\)06\(\backslash\)08\(\backslash\)11\(\backslash\)13) followed the order of Rest > Hwy > City. That is, the _bm_ feature value decreased as the stress increased. This verifies the findings of [PERSON] et al. (2019), indicating that negative emotions of stress negatively impact the coherence of heart-rhythm patterns. In this study, classification was discussed using one feature. For some driving datasets, two features, _avNN_ + _bmNN_ or _bmNN_ + _bmNN_, were used in combination to improve the classification accuracy.