## 4 Literature Review
### Combination of Signals  
The combination of more than two types of physiological signals provides complementary information that can improve sleep stage classification. This approach can be beneficial because certain features of a sleep stage might be missed by one signal but detected by another [30]. We have categorised studies into two further subcategories below based on the type of signal combination used as input. The first category includes studies that utilise a combination of four types of signals as input: EEG, EMG, ECG, and respiratory. The second category encompasses studies that employ a combination of three types of signals as input: EEG, EMG, and ECG, or a combination of EEG, EMG, and EOG. As mentioned earlier, the first category includes studies that utilise a combination of four types of signals: EEG, EMG, ECG, and respiratory as input to classify sleep stages. Only two studies were found in this category. [PERSON] et al. [88] proposed a model that extracted statistical features and utilised an SVM classifier, achieving an accuracy of 69% in classifying five sleep stages. Furthermore, [PERSON] et al. [89] extracted mean and variance features from raw data and employed a BLD classifier, resulting in an 80% accuracy for the classification of five sleep stages. The second category encompasses studies that employ a combination of three types of signals: EEG, EMG, and ECG/EOG as inputs to classify five sleep stages. [PERSON] et al. [90] extracted R-R features from ECG signals and applied fast Fourier transform (FFT) to extract frequency domain features from EEG and EMG signals. The selected features were then evaluated using a linear discriminant analysis (LDA) classifier, resulting in an accuracy of 80%. [PERSON] et al. [91], on the other hand, used a short-time Fourier transform method to extract frequency domain features. These features were evaluated by passing them through a model that included a combination of 1D-CNN layers and a bidirectional LSTM (Bi-LSTM) layer. Their model achieved a classification accuracy of 87.5%. In a study by [PERSON] et al. [92], the researchers investigated the utilization of five signal combinations, namely, ECG, EEG, EMG, left-eye EOG, and right-eye EOG. They explored all possible combinations of these signals and determined that the combination of EEG, EMG, and ECG exhibited the most promising outcomes. Statistical features were extracted from these signals, taking into account different window sizes and signal lengths, and an XGBoost classifier was employed to evaluate the performance. The proposed model achieved an accuracy of 85%. [PERSON] et al. [93] used two channels of EEG signals (C3-A2 and C4-A1), two channels of EOG signals (O1-A2 and LOC-A2), and one channel of EMG signals (X1). They applied fine-grained segmentation and a 2D-CNN model to classify five sleep stages. The architecture of the 2D-CNN model included two 2D-CNN layers, max-pooling layers, and a fully connected layer. The classification of five sleep stages by their model resulted in an accuracy of 90.12%. [PERSON] et al. [94] proposed a method that combined short-time Fourier transform features with raw data to classify five sleep stages using a 2D-CNN model. The architecture of their model consisted of two 2D-CNN layers, followed by a max-poolinglayer, an LSTM layer, and a fully connected layer. Their approach achieved an accuracy of 86%. [PERSON] et al. [95] proposed a 2D-CNN model to extract features from a combination of six channels of EEG signals and two channels of EOG signals. The 2D-CNN architecture consisted of three layers of 2D-CNN and max-pooling layers. Additionally, they utilised a separate 2D-CNN model to extract features from three channels of EMG signals. All the extracted features were then combined and passed to a fully connected layer for the classification of five sleep stages. The proposed model achieved an accuracy of 79%. [PERSON] et al.