## 4 Literature Review
### Electroencephalogram (EEG)  
EEG techniques capture the brain's electrical activity. Electrical signals in the brain can be measured by observing changes in the electrical activity between two electrodes over time. The standard method for measuring EEG signals is commonly known as the 10-20 system, which employs a minimum of 21 electrodes [54]. The EEG signal displays the diverse properties of brain activity. These activities help to classify sleep stages. Stage W is characterised by alpha activity in the occipital area [55]. The N1 stage is the transition between the W and N2 stages, and theta activity is one of the characteristics of EEG activity of the N1 stage [55]. Stage N2 is characterised by distinctive features known as spindles and K-complexes. spindles are brief bursts of high-frequency brain activities, while K-complexes are characterised by sharp, high-amplitude brain activity with a unique appearance in EEG signals [56]. Delta activity is indicative of N3, which is a deep sleep stage [57]. The REM stage is characterised by rapid, low-voltage theta waves [58]. The distinct frequency ranges of EEG signals corresponding to each sleep stage are shown in Table 1. Figure 3 shows a sample of time series EEG data for the five sleep stages.  
Table 2 lists studies that used two or more physiological signals for sleep stage classification. Few studies of sleep stage classification models have been developed utilising multiple channels of EEG signals as inputs. [PERSON] et al. [59] proposed a deep learning model to select important features from EEG signals for sleep stage classification, aiming to reduce reliance on sleep experts. They used two channels of EEG signals (Fpz-Cz and Pz-Oz) as input to the 1D-CNN model. The architecture of 1D-CNN included seven layers of 1D-CNN, a max-pooling layer followed by a fully connected layer to classify five sleep stages. Their model achieved an accuracy of 92.60%. Similarly, [PERSON] et al. [60] proposed a deep learning model, which used two channels of EEG signals (C3-A2 and C4-A1) as input to the 1D-CNN model. The architecture of 1D-CNN contained seven blocks that included 1D-CNN, batch normalization, and ReLu layers. The last layer was fully connected with softmax to classify the segments into five sleep stages. Their model was tested on subgroup 1 and subgroup 2 of the ISRUC-Sleep dataset, and the accuracies achieved for the classification of five sleep stages were 97.22% and 95.06%, respectively.  
Another study by [PERSON] et al. [61] extracted features from two channels of EEG signals (Pz-Oz, Fpz-Cz) by using an FFT method to improve the accuracy of the classification. The features extracted passed to SVM to classify three sleep stages and  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**Sleep Stage** & **Characteristic Frequency** \\ \hline W & Alpha (8–12 Hz) \\ \hline N1 & Theta (4–8 Hz) \\ \hline N2 & Spindle and K-complexes (12–15 Hz) \\ \hline N3 & Delta (0.5–4 Hz) \\ \hline REM & Alpha (8–12 Hz) \\ Theta (4–8 Hz) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Distinct frequency ranges of EEG signals corresponding to each sleep stage.  
five sleep stages. Their model achieved an accuracy of 94.14% and 91.73% for three- and five-sleep-stage classification, respectively. [PERSON] et al. [62] conducted a study to explore the utilization of time-frequency representations, such as spectrograms, as input for a fine-tuned VGG-16 network. Their research focused on comparing various spectrograms encoding multiple EEG channels to facilitate the recognition of visual patterns in images. The study reported an achieved accuracy of 82.96% with five-sleep-stage classification.