## 4 Results and Discussion
### Classification Results Using Proposed Novel HML Classifier  
The performance for the classification of the subjects is shown in Table 2. Previously, we designed the diagnostic of bruxism sleep syndrome and sleep stages using a decision tree classifier with 81.25% accuracy [19]. [PERSON] et al. [40] showed that the ECG channel and EMG channel are used for the detection of bruxism syndrome using power spectral density (PSD) techniques. They used the 488-min data for the two channels and classification of the subjects and stages using the machine learning method. Our proposed system, the C4-A1 channel, has the best performance in the subjects (bruxism and healthy) classification based on sensitivity, specificity, and accuracy, which are 95%, 92%, and 94%, respectively (Figure 7). Figure 5: Power spectral analysis of the channels such as (**A**) the ECG1-ECG2 channel, (**B**) EMG1-EMG2 channel, (**C**) C4-P4 channel, and (**D**) C4-A1 channel of the bruxism subjects. Figure 6: Power spectral analysis of the channels such as (**A**) the ECG1-ECG2 channel, (**B**) EMG1-EMG2 channel, (**C**) C4-P4 channel, and (**D**) C4-A1 channel of the healthy subjects. The performance of the sleep stages classification is shown in Table 3. Previously, the unsupervised learning architecture and hidden Markov model were used for the detection of sleep stages [58]. [PERSON] et al. [59] used a multimodal sensor system evaluating hand acceleration, ECG, and ActiWatch for diagnosing sleep stages such as w, REM, and NREM. [PERSON] et al. [60] designed an automatic system for diagnosing sleep stages using time-frequency images of the EEG signals. [PERSON] et al. [61] studied that heart rate measurement is helpful and easy to use in sleep stage monitoring. They successfully calculated four stages and overall five stages with 66% accuracy of the system. Our proposed system, the ECG1-ECG2 channel, has the best performance in the sleep stages (w and REM) classification based on sensitivity, specificity, and accuracy, which are 100, 86, and 95%, respectively (Figure 8). \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Name of the Channel** & **PT** & **NT** & **TP** & **TN** & **FP** & **FN** & **F1** & **MCC** & **Sen** & **Spe** & **Acc** \\ \hline EMG1-EMG2 & 64 & 17 & 46 & 17 & 18 & 0 & 0.83 & 0.59 & 1 & 0.48 & 0.77 \\ ECG1-ECG2 & 78 & 3 & 46 & 3 & 32 & 0 & 0.74 & 0.22 & 1 & 0.08 & 0.60 \\ C4-P4 & 48 & 26 & 45 & 24 & 3 & 2 & 0.94 & 0.85 & 0.95 & 0.88 & 0.93 \\
**C4-A1** & 47 & 27 & 45 & 25 & 2 & 2 & **0.95** & **0.88** & **0.95** & **0.92** & **0.94** \\ \hline Mean & 59.2 & 18.2 & 45.5 & 17.2 & 13.7 & 1 & 0.86 & 0.63 & 0.97 & 0.59 & 0.81 \\ \(\pm\)SD & 14.7 & 11.1 & 0.57 & 10.1 & 14.1 & 1.15 & 0.09 & 0.30 & 0.02 & 0.39 & 0.16 \\ \hline \hline \end{tabular} PT: positive test; NT: negative test; TP: true positive; TN: true negative; FP: false positive; FN: false negative; MCC: Matthews correlation coefficient; Sen: sensitivity; Spe: specificity; Acc: accuracy. Bold is the best model.  
\end{table} 
Table 2: Performance of the subjects (bruxism and healthy) classification using the novel 20-fold cross-validation model of the HML classifier. Figure 7: Performance analysis of the subjects (bruxism and healthy) classification using the novel 20-fold HML classifier. (**A**) Comparison between all channels such as EMG1-EMG2, ECG1, ECG2, C4-P4, and C4-A1 channels of the subjects classification. In addition, (**B**) highest and mean performance of the subjects classification. The C4-A1 channel of the EEG signal has the highest accuracy (94%) in the subjects classification. The performances for the combination of subjects and sleep stages classification are shown in Table 4. Our proposed system, the C4-P4 channel, has the best performance in the combined (subjects and sleep stages) classification based on sensitivity, specificity, and accuracy, which are 98, 90, and 97%, respectively (Figure 9).