## 4 Literature Review
### Combination of Signals
[74] proposed a model that combined a short-time Fourier transform method with multitask CNN layers for the classification of five sleep stages. The multitask CNN architecture comprised one layer of 2D-CNN, a max-pooling layer, and a multitask softmax layer. The model used a combination of one EEG signal channel (C4-A1), one EOG signal channel (ROC-LOC), and two EMG signal channels (CHIN1-CHIN2) as input, and the model achieved an accuracy of 81.2%. [PERSON] et al. [96] utilised a combination of EEG signals (Fpz-Cz and Pz-Oz), an EOG signal (Horizontal), and an EMG signal as input. To extract features from raw data, they employed a 1D-CNN model consisting of a convolution block (four 1D-CNN layers and max-pooling layers) and a reduction block (input passed to two max-pooling layers and three layers of 1D-CNN). These blocks were repeated four times, followed by two 1D-CNN layers and a fully connected layer. The model's performance was evaluated on two datasets, achieving an accuracy of 85.40% on the Sleep-edf dataset and 81.60% on the Sleep-edfx dataset for classifying five sleep stages. Similarly, [PERSON] et al. [97] utilised two channels of EEG signals (C3-A2 and C4-A1), one channel of an EMG signal, and two channels of EOG signals (EOG-L and EOG-R) as input. They applied a wavelet decomposition method to extract frequency domain features and evaluated the performance of these features by using a bagging tree classifier for the classification of three and five sleep stages. The model achieved an accuracy of 95.44% and 95.20% for the classification of three and five sleep stages, respectively. [PERSON] et al. [98] utilised a combination of EEG, EMG, and EOG signals to feed into a 1D-CNN model with four layers of 1D-CNN, followed by max-pooling layers, achieving an accuracy of 73% for the classification of five sleep stages. Then, they applied an STFT method to extract frequency domain features from the raw data. These features were passed to the 1D-CNN model, which resulted in an improved accuracy of 74.24%. [PERSON] et al. [67] utilised two datasets, Sleep-edfx and ISRUC-Sleep, to evaluate their SSNet model's performance for the classification of three and five sleep stages. From the Sleep-edfx dataset, they chose two EEG channels (Fpz-Cz and Pz-Cz), one EMG channel, and one EOG channel as input. Meanwhile, from the ISRUC-Sleep dataset, they selected two EEG channels (C3-A2 and C4-A1), one EMG channel (X1), and two EOG channels (O1-A2 and LOC-A2) as input. Their SSNet model achieved accuracies of 94.64% and 91.22% for three- and five-sleep-stage classification on the Sleep-edfx dataset, respectively. On the ISRUC-Sleep dataset, the SSNet model reported accuracies of 94.34% and 90.98% for three and five sleep stages, respectively. [PERSON] et al. [99] utilised EEG (C3-A2), EMG (X1), and EOG (ROC-A2) signals as input, and passed them to a 1D-CNN model consisting of nine layers. Their model demonstrated high accuracy for the classification of three and five sleep stages of subgroup 1 of the ISRUC-Sleep dataset, with reported accuracies of 98.61% and 98.46%, respectively. Furthermore, the model was tested on subgroup 2 of the ISRUC-Sleep dataset, yielding accuracies of 98.78% and 98.46% for the classification of three and five sleep stages, respectively. Another study by [PERSON] et al. [100] employed the same signals as in their previous study [99] and extracted statistical features to pass to an RF classifier. Their model classified five sleep stages on subgroups 1 and 2 of the ISRUC-Sleep dataset, achieving accuracies of 98.52% and 98.46%, respectively. [PERSON] et al. [101] proposed a model for sleep stage classification, which aims to classify five sleep stages using features extracted from four distinct channel signals, namely, EEG (Fpz-Cz, Pz-Oz), EOG, and EMG signalsobtained from PSG recording. The model architecture consists of two key building blocks: the \"Conv Block\" and the \"Bi-LSTM Block\".