## 4 Literature Review
### Electrooculogram (EOG)  
In sleep research, the measurement of eye movements is crucial for evaluating sleep quality and identifying sleep stages [68]. EOG recording is used to capture eye movements: Surface electrodes are positioned around the eye to measure a potential gap between its anterior and posterior poles [69]. During the W stage, EOG signals are used to detect rapid eye movements, providing an indication of this stage's characteristics. In the NREM stage, EOG signals record slow eye movements. In contrast, during the REM stage, EOG signals can capture bursts of rapid eye movements, which are a distinctive feature of this stage [70]. Figure 5 presents a sample of time series EOG data for the five sleep stages. Figure 4: Samples of EMG patterns in five sleep stages from the Sleep-edfx dataset [36]. Muscle activity exhibits a gradual reduction from the wake (W) stage to the REM (rapid eye movement) stage. The characteristics of EOG signals have been utilised in proposing models for classifying sleep stages. [PERSON] et al. [71] used a feature engineering method with fuzzy rules for the classification. Two channels of EOG and EMG signals were combined and passed to an FFT method to extract features. Fuzzy rules were used to predict the final results for the classification of five sleep stages. The study did not report the performance of their proposed model. [PERSON] et al. [72] proposed a deep learning model to extract features from a combination of EEG and EOG signals. They selected one channel of EEG signal (Fpz-Cz) and one channel of EOG signal (horizontal). These features passed to a 1D-CNN model. Their architecture consisted of two layers of 1D-CNN and max-pooling layers, and the order of these layers was repeated five times. The final layers were two fully connected layers to classify the segments into three and five sleep stages. They tested their model on two datasets (Sleep-edf and Sleep-edfx). The model achieved an accuracy of 94.64% for three sleep stages and 91.22% for five sleep stages on the Sleep-edf dataset. Similarly, the model achieved an accuracy of 94.34% for three sleep stages and 90.98% for five stages on the Sleep-edfx dataset. A study by [PERSON] et al. [73] proposed a deep learning model that contained a deep network to improve the classification accuracy. Their model inputs two channels of EEG signals (Fpz-Cz and Pz-Cz) and one channel of EOG signal. Their architecture consisted of six layers of 1D-CNN, followed by batch-normalization and max-pooling layers. After that, they added three layers of 1D-CNN, followed by a batch normalization and max-pooling layer. This structure was repeated three times. In the end, they added two max-pooling layers, followed by a 1D-CNN layer, a max-pooling layer, a 1D-CNN layer, and two fully connected layers. Their model achieved an accuracy of 81% for the classification of five sleep stages. [PERSON] et al. [74] used two different datasets (Sleep-edf and MASS) for evaluating their proposed model. They selected a combination of one channel of  
Figure 5: Samples of EOG patterns in five sleep stages from the Sleep-edfx dataset [36]. Wake (W) shows frequent eye movements, the NREM stages display sporadic eye movements and unique patterns, and the REM stage exhibits rapid distinct eye movements. EEG signal (Fpz-Cz) and one channel of EOG (horizontal) signal from the Sleep-edf dataset. Similarly, they used a combination of one channel of EEG (C4-A1) and one channel of EOG signal (ROC-LOC) from the MASS dataset. They used a short-time Fourier transform method and a 2D-CNN model to classify five sleep stages. The architecture of their model consisted of one layer of 2D-CNN, a max-polling layer, and a multitask softmax layer. Their model achieved an accuracy of 82.30% on the Sleep-edf dataset and 82.50% on the MASS dataset. [PERSON] et al. [67] proposed an SSNet model using a combination of signals of two channels of EEG (Fpz-Cz and Pz-Cz) and one channel of EOG as input.