## 2 Literature Survey  
Since polysomnography was established as the primary diagnostic method for measuring sleep duration and evaluating the quality of sleep in the 1980s, researchers from all over the world have carried out many studies to advance sleep classification approaches. The medical field continues to be quite interesting. Considerable effort has been made in this regard by evaluating different classifiers over a range of input data to increase the accuracy attained by employing such artificially constructed models. Table 2 shows related studies using deep learning for sleep stage classification. Many studies have been limited by the availability of high-quality and sufficient datasets. Limited access to annotated sleep data specifically for sleep disorders or diverse populations can hinder the development and evaluation of classification algorithms. Some studies may lack external validation or independent datasets, limiting the generalization of the proposed algorithms. Models trained and tested on specific datasets may not perform well on different datasets collected using different protocols or from different populations. For instance, a CNN model was suggested by [PERSON] et al. [13] to remove the need for feature engineering by extracting features from EEG and electroocologram (EOG) segments. Ten 1D-CNN layers and a fully connected layer were used. The accuracy of their model was 94.24% in the three-sleep-stage classification and 90.98% in the five-sleep-stage classification. A CNN method was used by [PERSON] et al. [14] to categorize the five phases of sleep. Their design comprised three 1D-CNN layers: max pooling and dropout layers follow the first layer, max pooling and batch normalization follow the second layer, and max pooling and two fully connected layers follow the final layer. With an accuracy rate of 87.67%, their method successfully identified the five phases of sleep. [PERSON] et al. [15] introduced an innovative deep learning-based method for automatically classifying different phases of sleep. The proposed method uses a single-channel EEG signal to divide classes. During the preprocessing stage, data augmentation is used to balance the number of epochs that correspond to different sleep phases. The proposed method for classifying two to six classes achieved an accuracy of more than 90%, which is superior to that of other existing methods. [PERSON] et al. [16] suggested a deep learning model using the attention mechanism and a 1D-CNN. It was found that the accuracy of classification for the five stages of sleep was 82.80% for Sleep-EdfX and 93.7% for Sleep-Edf datasets. [PERSON] and [PERSON], 2020 [17], proposed an effective approach for sleep stage classification utilizing electroencephalogram (EEG) data processing and machine learning classifiers spanning 10s epochs. EEG signals are considered to have an important role in automated sleep stage classification. A band-pass filter partitions EEG data into frequency sub-bands. Statistical features are extracted and trained using decision tree (DT), support vector machine (SVM), and random forest (RF) techniques, with various testing dataset percentages. The findings based on the Sleep-Edf dataset show that the random forest algorithm achieved 97.8% accuracy. [PERSON] et al. [18] suggested a CNN model that uses no feature engineering techniques to extract features from EEG, EMG, and EOG segments. They employed two 2D-CNN layers, max pooling, and a fully connected layer as the final layer. Their model's accuracy at classifying the five stages of sleep was 92% on a subject-wise test set. [PERSON] et al. [19] proposed a 2D-CNN model and the fast Fourier transform (FFT) technique for classifying sleep stages into five stages. Their model's accuracy on a subject-specific test set was 83.6%. [PERSON] et al. [20] suggested a deep learning and machine learning-based sleep staging paradigm. Utilizing 80:20 percentages for training and testing data, the proposed approach was trained on sleep-related diseases and healthy control patients.