## 4 Conclusions  
In this study, a T-MCCFNN was proposed for the automatic classification of sleep stage detection. In this study, the Sleep-EDF database was used to verify the performance of the proposed model. The experimental results indicate that the accuracy and F1-score of the proposed T-MCCFNN in sleep stage classification are 85.3% and 85.3%, respectively, which is better than the methods proposed by other methods. In addition, we have also shown that the compensatory FNN has a better convergence speed than the general FNN, and the model with multi-scale convolution kernels has better accuracy, recall, precision, and F1-score than the model with single-scale convolution kernels. The major contributions of this study were as follows:  
* Multiscale convolution kernels in the T-MCCFNN model were used to extract input EEG signal features.
* A compensatory FNN was used in place of the traditional FCN classifier to improve the learning convergence speed and reduce model parameters.
* The Taguchi method was used to find the optimal parameter combination through minimal experimentation.
* The experimental results indicate that the accuracy, recall, precision, and F1-score of the proposed T-MCCFNN in sleep stage classification were 85.3%, 83.3%, 87.3%,  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Accuracy**} & \multirow{2}{*}{**F1-Score**} & \multicolumn{6}{c}{**Per-Class F1-Score**} \\ \cline{3-8}  & & & **W** & **N1** & **N2** & **N3** & **REM** \\ \hline [PERSON] et al. [29] & 82.0\% & 76.9\% & 84.7\% & 46.6\% & 85.9\% & 84.8\% & 82.4\% \\ [PERSON] et al. [30] & 82.6\% & 76.3\% & 81.6\% & 42.4\% & 87.4\% & 87.9\% & 82.1\% \\ [PERSON] et al. [31] & 83.9\% & 78.7\% & 81.6\% & 52.9\% & 88.1\% & 85.3\% & 85.4\% \\ [PERSON] et al. [32] & 84.4\% & 78.1\% & 89.7\% & 42.6\% & 88.8\% & 90.2\% & 79.0\% \\ [PERSON] et al. [33] & 83.8\% & 75.3\% & 90.0\% & 31.0\% & 88.0\% & 89.0\% & 78.0\% \\ MCCFNN & 84.4\% & 84.5\% & 89.6\% & 39.2\% & 92.1\% & 88.2\% & 76.3\% \\ T-MCCFNN & 85.3\% & 85.3\% & 92.7\% & 35.9\% & 88.5\% & 91.2\% & 84.8\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison results of various deep learning methods.  
and 85.3%, respectively, which is superior to the scores of the methods proposed by other scholars.  
To detect the patient's sleep quality in real time, the proposed T-MCCFNN model will be deployed into the Field-Programmable Gate Array (FPGA) in future work. Because FPGA is known for its excellent performance and low power consumption, it is very suitable to be embedded in wearable devices to assist doctors in instantly assessing patients' sleep quality.  
Conceptualization, C.-J.L. ([PERSON]) and X.-Q.L.; Methodology, C.-J.L. ([PERSON]) and C.-J.L. ([PERSON]); Software, C.-J.L. ([PERSON]) and X.-Q.L.; Validation, C.-J.L. ([PERSON]); Data curation, X.-Q.L.; Writing--original draft, C.-J.L. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Science and Technology of the Republic of China, grant number NSTC 112-2221-E-167-026.  
Not applicable.  
The authors confirm that the data supporting the findings of this study are available within the paper.  
The authors declare no conflict of interest.