## 4 Literature Review
### Combination of Signals
The Conv Block includes two consecutive 1D convolutional layers, a max-pooling layer, and a dropout layer for extracting spatial features from the input signals. On the other hand, the Bi-LSTM Block comprises a Bi-LSTM layer, a max-pooling layer, and a dropout layer to capture and learn temporal correlations in the data. By concatenating the outputs of these dual-channel convolutional Bi-LSTM network modules, the model classifies the five sleep stages and reported an accuracy of 91.44% in their study. Later, [PERSON] et al. (2012) proposed a hybrid model that combined multiple signals, including EEG (C4-A1), EOG (EOGL and EOGR), and EMG signals. They fed the signals to a model architecture that consisted of seven layers of 1D-CNN and GRU. They tested their model on the SHHS dataset, utilising 717,883 segments, and their model achieved an accuracy of 83.15%. [PERSON] et al. (2013) proposed a DeConvolution- and Self-Attention-based Model (DCSAM) as a novel approach for the classification of five sleep stages. DCSAM has the capability to reverse the feature map of a hidden layer, mapping it back to the input space. The DCSAM model comprises five layers of 1D-CNN, followed by max-pooling layers. The final two layers consist of an attention layer and a fully connected layer. Their model achieved an accuracy rate of 90.26%. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Sube** & **Another/Year** & **Dataset** & **Number of Sum-** & **Spains** & **Number of Channels** & **Input** & **Classification** & **Number of Classes** & **Accuracy** & **Kepps** & **Splitting Strategy** \\ \hline
1 & [PERSON] et al., 2002 [20] & Private & 11 recordings & KOC, BMC, EEG & - & FFT & Fuzzy rule & 5 & - & - & - \\ \hline
2 & [PERSON] et al., 2006 [7] & Private & 10 recordings & KOC, BMC & 2 & FFT & Fuzzy rule & 5 & - & - & - \\ \hline
3 & [PERSON] et al., 2008 [8] & Private & 37 recordings & KOC, BMC & 2 & WT & DNN & 3 & - & 96.00 & 30\% training + 50\% testing \\ \hline
4 & [PERSON] et al., 2012 [6] & Private & 4 recordings & EEG, ECG & 2 & FFT & SVM & 5 & 99.00 & - & - \\ \hline
5 & [PERSON] et al., 2014 [5] & Private & 115 recordings & ECG, regulatory & - & Statistic features & LD & 2 & 95.00 & 90.0 & - \\ \hline
6 & [PERSON] et al., 2014 [8] & Private & 35,128 samples & ECG, EMG, & - & WT & SVM & 5 & 99.00 & 69.50 & - \\ \hline
7 & [PERSON] et al., 2015 [90] & Private & 10 recordings & KOC, BMC, & 3 & Statistic features & BLD & 3 & 80.00 & & \\ \hline
8 & [PERSON] et al., 2015 [85] & Private & - & ECG, regulatory & 2 & Statistical features & BLD & 3 & 80.00 & 49.00 & - \\ \hline
9 & [PERSON] et al., 2018 [64] & Sleep-off & 5 recordings & EEG, EMG & 2 & TD & SVM & 5 & 99.80 & 94.00 & 10-fold \\ \hline
10 & [PERSON] et al., 2018 [64] & Private & 41 recordings & EEG, ECG, EMG & - & RS-FFT & LD & 5 & 80.00 & - & - \\ \hline
11 & [PERSON] et al., 2018 [85] & BSMC-G  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline
**S\_No** & **Another/Year** & **Dataset** & \begin{tabular}{c} **Number of Samples** \\ **(pleWave/degrees)** \\ \end{tabular}  & **Signals** & \ \begin{tabular}{c} **Number of** \\ **Channel** \\ \end{tabular}  & **Impat** & **Classification** & \begin{tabular}{c} **Number** \\ **of Classes** \\ \end{tabular}  & **Accuracy** & **Kappa** & **Splitting Strategy** \\ \hline
21 & \begin{tabular}{c} Satisfyably et al., 2021 [64] \\ \end{tabular}  & \begin{tabular}{c} Satisfyably 1 \\ \end{tabular}  & 6000 samples & EIG & 2 & Raw data & 1D-CNN & 5 & \begin{tabular}{c} 97.22 \\ 95.06 \\ \end{tabular}  & - & Training 70\%, testing 30\% \\ \hline  & \begin{tabular}{c} [PERSON] et al., 2021 [64] \\ \end{tabular}  & \begin{tabular}{c} Satisfyably 2 \\ \end{tabular}  & 6000 samples & EIG & 2 & Raw data & 1D-CNN & 5 & \begin{tabular}{c} 72.22 \\ 95.06 \\ \end{tabular}  & - & Training 70\%, testing 30\% \\ \hline  & \begin{tabular}{c} EIG, ECG \\ \end{tabular}  & \begin{tabular}{c} EIG, ECG \\ \end{tabular}  & 2 & \ \begin{tabular}{c} Satisfy-features-FFT \\ \end{tabular}  & RF & 5 &