## 5 Sleep Stages Classification Using DL Models
### DL Techniques Used in Automatic Sleep Stage Classification
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Database** & **Author** & **Signals** & **Samples** & **Approach** & \
\begin{tabular}{c} **Tools/Programming** \\ **Languages** \\ \end{tabular} &
\begin{tabular}{c} **Accuracy** \\ **(\%)** \\ \end{tabular} \\ \hline
**MIT-BIH** & \
\begin{tabular}{c} [PERSON] et al. [86] 2020 \\ [PERSON] et al. [87] 2018 \\ [PERSON] et al. [88] 2018 \\ [PERSON] et al. [70] 2020 \\ [PERSON] et al. [89] 2019 \\ [PERSON] et al. [90] 2019 \\ [PERSON] et al. [90] 2019 \\ [PERSON] et al. [90] 2019 \\ SäHHS \\ [PERSON] et al. [60] 2018 \\ [PERSON] et al. [61] 2018 \\ \end{tabular} &
\begin{tabular}{c} EEG & – \\ EEG & – \\ EEG & – \\ EEG & – \\ EEG & – \\ EEG & – \\ EEG & – \\ EEG & – \\ EEG & – \\ EEG & – \\ EEG & – \\ EEG & – \\ EEG & – \\ EEG & – \\ EEG & – \\ EEG & – \\ EEG & – \\ EEG & – \\ EEG & – \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of automated sleep stage classification approaches with DL applied to PS recordings in Sleep Heart Health Study (SHHS) and Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) datasets. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Author** & **Signals** & **Samples** & **Approach** & \begin{tabular}{c} **Tools/Programming** \\ **Languages** \\ \end{tabular}  & **Accuracy (\%)** \\ \hline \begin{tabular}{c} [PERSON] et al. [78] 2018 \\ [PERSON] et al. [78] 2018 \\ [PERSON] et al. \\ [79] 2020 \\ [PERSON] et al. [65] 2019 \\ [PERSON] et al. [80] 2020 \\ [PERSON] et al. [63] 2020 \\ [PERSON] et al. [69] 2019 \\ [PERSON] et al. [81] 2016 \\ [PERSON] et al. [65] 2019 \\ [PERSON] et al. [65] 2019 \\ [PERSON] et al. [82] 2019 \\ \end{tabular}  & \begin{tabular}{c} EEG & – \\ EEG & – \\ EEG & – \\ EEG & – \\ EEG & – \\ EEG & – \\ EEG & – \\ EEG & – \\ EEG & – \\ EEG & – \\ EEG & – \\ EEG & – \\ EEG & – \\ EEG & – \\ EEG & – \\ \end{tabular}  & \begin{tabular}{c} – \\ - \\ RNN-biLSTM & – \\ - \\ EEG & – \\ EEG & – \\ EEG & – \\ EEG & – \\ EEG & – \\ EEG & – \\ EEG & – \\ EEG & – \\ EEG & – \\ EEG & – \\ EEG & – \\ EEG + & – \\ \end{tabular}  & \ \begin{tabular}{c} – \\ - \\ - \\ - \\ - \\ - \\ EEG & – \\ EEG & – \\ EEG & – \\ EEG & – \\ EEG & – \\ EEG & – \\ EEG & – \\ EEG & – \\ EEG & – \\ \end{tabular}  &
\begin{tabular}{c} –Figure 8 shows the number of times PSG recordings such as EEG, EOG, EMG, and ECG signals were used for sleep stage classification studies. It is not surprising that EEG signal was the most popular input for DL models. The characteristic waves and description of each sleep stages are often based on EEG characteristics (i.e., alpha waves, theta waves, delta waves, etc. ); Figure 5.  
Nonetheless, other signals within the PSG recordings are indispensable, because they provide additional information on biological aspects of sleep that may not be manifested in EEG recordings. Since REM sleep is characterized by the movement of eyes and loss in muscle tone of the body core, EOG, and EMG signals may provide key information to separate the REM sleep stage from the other stages. It was shown that some of the REM sleep stages could be overlooked in single-channel EEG input [27]. Therefore, a combination of signals, comprising of EOG, EMG, and EEG, are second in terms of frequency of use after single-channel EEG inputs (Figure 8).