## 4 Results and Discussion
### Application and Limitation of the Proposed System  
The proposed work showed an application for the detection of bruxism disorder and sleep stages using ECG1-ECG2, EMG1-EMG2, C4-P4, and C4-A1 channels. This work would provide a more  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Year** & **Disease** & **Acc (\%)** \\ \hline [PERSON] et al. [71] & 2020 & Cancer & 97 \\ [PERSON] et al. [72] & 2019 & Heart & 88 \\ [PERSON] et al. [73] & 2019 & Heart & 90 \\ [PERSON] et al. [74] & 2010 & Schizophrenia & 87 \\ [PERSON] et al. [75] & 2007 & Cushing & 92 \\ \hline \multirow{2}{*}{**Proposed**} & & & 94 \\  & & **Bruxism** & 95 \\ \end{tabular} \
\begin{tabular}{c} **P** \\ \end{tabular}
\begin{tabular}{c} **B** \\ \end{tabular} \\ \hline \hline \end{tabular} Bold is the best model.  
\end{table}
Table 6: Comparison of the proposed HML classifier with the existing hybrid classifiers.  
Figure 10: A comparison of the (**A**) proposed method with the existing methods and (**B**) proposed HML classifier with the existing hybrid classifiers.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Reference** & **Year** & **Disease** & **Signal** & **Classifier** & **Sen (\%)** & **Spe (\%)** & **Acc (\%)** \\ \hline [PERSON] et al. [40] & 2019 & Bruxism & EEG & DT & 89 & 78 & 81 \\ [PERSON] et al. [19] & 2019 & Bruxism & EMG & DT & 94 & 92 & 93 \\ [PERSON] et al. [65] & 2019 & SA & EEG & KNN & 98 & 83 & 91 \\ [PERSON] et al. [66] & 2019 & OSA & ECG & SVM RF Kernel & 94 & 94 & 94 \\ [PERSON] et al. [67] & 2018 & OSA & ECG & Threshold & 88 & 90 & 90 \\ [PERSON] et al. [68] & 2017 & Sleep Stage & EEG, EMG & Threshold & 81 & 93 & 81 \\ [PERSON] et al. [69] & 2008 & Sleep Stage & EEG, EMG & Threshold & 71 & 96 & 84 \\ [PERSON] et al. [70] & 2004 & Sleep Stage & EEG, EMG & Threshold & 66 & 84 & 82 \\ \hline \multirow{4}{*}{**Proposed**} & \multicolumn{2}{c}{**Subjects**} & **EEG** & & 95 & 92 & **94** \\  & \multicolumn{2}{c}{(Bruxism and Healthy)} & \multicolumn{2}{c}{(C4-A1)} & \multicolumn{2}{c}{**HML**} & & \\ \cline{1-1}  & \multicolumn{2}{c}{**Sleep Stages**} & **ECG** & & & 100 & 86 & **95** \\ \cline{1-1}  & \multicolumn{2}{c}{(w and REM)} & \multicolumn{2}{c}{(ECG1-ECG2)} & & & & & \\ \multicolumn{1}{c}{**Combine**} & \multicolumn{2}{c}{**EEG**} & & & 98 & 90 & **97** \\ \cline{1-1}  & \multicolumn{2}{c}{(Subjects and Sleep Stages)} & \multicolumn{2}{c}{(C4-P4)} & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of the proposed method with the existing methods.  
effective and accurate detection system of bruxism and sleep stages for medical application. The most important application of the present research is to diagnose people with a mental health condition in a fast and accurate manner. This system also helps to find the problem during the sleep stage.  
The present work has some limitations in that the proposed data from the _physionet_ website were old and small for the evaluation. Further work could be required for a large number of real-time data to analyze the present work for higher accuracy. Secondly, the proposed physiological signals, including ECG, EEG, and EMG, did not cover all sleep recordings. Moreover, we used two sleep stages, including w and REM. In the future, we will use different signals and different sleep stages for the detection of bruxism sleep disorders.