## 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
**USDIVE** & [PERSON] et al. [91] 2018 & EEG & – & CNN & – & 92.2 \\  & [PERSON] et al. [92] 2018 & EEG & – & CNN-LSTM & – & – \\  & [PERSON] et al. [86] 2020 & EEG & – & Orthogonal & – & 88.4 \\
**UCD** & [PERSON] et al. [87] 2018 & EEG & – & CUCNN & MATLAB & 87.0 \\  & & Multivariate & 287,840 & Hybrid CNN & PyTorch & 74.2 \\  & [PERSON] et al. [71] 2020 & EEG & 264,736 & CNN & – & 96.0 \\  & [PERSON] et al. [94] 2018 & PSG signals & 10,000 & RCNN & PyTorch & 87.5 \\  & [PERSON] et al. [95] 2017 & EEG & 10,000 & RCNN & TensorFlow flow & 85.7 \\  & [PERSON] et al. [62] 2019 & ECG HRV & 541,214 & LSTM & – & 77.0 \\ \hline \hline \end{tabular}  
* The accuracy scores in Tables 1–5 are based on AASM guidelines, five class classification [21].  
\end{table} 
Table 5: Summary of automated sleep stage classification approaches with DL applied to PSG recordings in ISRUC, Massachusetts General Hospital (MGH), and University College Dublin Sleep Apnea Database (UCD) datasets. Figure 8: Different subsets of PSG recordings used to train DL models for automated sleep stage classification as listed in Tables 1–5. Of the 36 studies, the mixture of signals (electroocologram (EOG), electromyogram (EMG), and electroencephalography (EEG)) was employed 14 times while EEG signals were used 28 times. Only a small fraction (five studies) employed ECG or EOG time series. * Summary statistics: using EEG versus EEG + additional signals. Although ECG is an important sleep parameter [96], it is not common to use raw ECG signals as a direct input for DL models. As seen in Table 4, heart rate variability (HRV) parameters derived from ECG signals, were used to train the DL models instead. There are only three studies that employed HRV parameters, and these studies classified sleep into four stages instead of five: wakefulness (W), light sleep (S1 and S2), deep sleep (S3 and S4), and REM sleep. [PERSON] et al. [60] proposed a 3-layer CNN model. They used a cardiorespiratory coupling (CRC) spectrogram, which was derived from ECG and HRV. Besides alternations in physiological signals, there are other changes in body system changes in some individuals such as cardiovascular [97], respiratory [98], or blood flow in the brain [99]. Hence, the CRC picks up the cardiovascular and respiratory changes. Their model achieved an overall accuracy of 65.9% and 75.4% for SHHS and MIT-BIH respectively, as seen in Table 4. [PERSON] et al. [61] combined EEG and HRV features as input to an AE model. During testing, the model achieved an overall accuracy of 73.7%. [PERSON] et al. [62] published the only study that was based on ECG signals from a private dataset that was collected as part of the European Union SIESTA project [100] as shown in Table 5. Likewise, they converted ECG signals into HRV and used the HRV features to train an LSTM model, which achieved an accuracy of 77.0%.