## 4 Literature Review
### Electrocardiogram (ECG) and Respiratory  
ECG and respiratory signals can assist with sleep stage classification because the heart rate and respiratory effort change throughout the sleep stages [77]. An ECG is a recording of the heart's electrical activity over a timespan. The ECG signal consists of several beats comprising the P wave, QRS complex, and T wave, depending on the individual's heart condition [78; 79]. The properties of the ECG signal can change during both NREM and REM sleep stages. The heart rate decreases during the NREM sleep stage, while heart rate variability can either increase or decrease depending on the NREM sleep stage. Conversely, both the heart rate and heart rate variability increase during the REM sleep stage, as reported in [80].  
Respiratory inductance plethysmography (RIP) is a noninvasive technique for measuring airflow and respiratory effort. Changes in respiratory patterns can help classify sleep stages. For example, during the REM sleep stage, the respiratory system can become more irregular, and the upper airway muscles may become more relaxed, leading to more frequent disruptions in breathing and potential sleep apnoea [81].  
We have categorised studies into two subgroups below based on the type of signal combination used as input. The first group includes studies utilising a combination of ECG and respiratory as input. The second group encompasses studies employing a combination of EEG and ECG or a combination of EEG and respiratory as input.  
As mentioned earlier, the first group includes studies that utilised a combination of ECG and respiratory as input. [PERSON] et al. [82] used statistical features of dynamic wrapping to extract features from ECG and respiratory signals and achieved a 95% accuracy in the binary classification of sleep stages using the LDA classifier. [PERSON] et al. [83] applied the Pan-Tompkins algorithm to extract an R-R interval from ECG signals and the mean and variance of respiratory signals to classify three sleep stages with an accuracy of 80% by using a BLD classifier. [PERSON] et al. [84] utilised a combination of signals from ECG and respiratory effort to classify the segments into the binary classification of sleep stages using a two-layered gated recurrent unit (GRU) neural network. They reported achieving an accuracy of 90.13%.  
The second group includes studies that utilised a combination of EEG and ECG or a combination of EEG and respiratory as input. For example, [PERSON] et al. [85] used R-R intervals from the ECG signal and the dispersion entropy method for extracting statistical features from the EEG signal, achieving a 73.70% accuracy in classifying five sleep stages using multi-fully-connected layers. [PERSON] et al. [86] used a fast Fourier transform method to extract features from one channel of EEG and ECG signals, achieving an accuracy of 99% in classifying five sleep stages using SVM. [PERSON] et al. [64] proposed a model that takes one EEG (F3-M2) and one respiratory signal channel as input. The model uses an FFT method to extract features from the EEG signal and extract statistical features such as mean, skew, and variance from the respiratory signal. The model achieved accuracies of 93.72% and 52.27% using RF and MLP classifiers, respectively. Moreover, they tested their model by combining one channel of EEG and ECG signals, using an FFT method to extract features from the EEG signal and R-R intervals from the ECG signal. Their proposed model achieved accuracies of 72.52% and 60.28% using RF and MLP classifiers, respectively. [PERSON] et al. [87] used a combination of two channels of EEG and ECG as input. They passed these signals separately to a 1D-CNN model, which contains five layers of 1D-CNN. The model they developed achieved an accuracy of 98.84% in a binary sleep stage classification.