# Hybrid Deep Learning Approach for Automated Sleep Cycle Analysis
## 2 Related Works  
Classifying sleep stages using PSG recordings has been the objective of considerable research. Several works focus on EEG signals, as they provide valuable frequency-based insights into brain function in all stages of sleep. These signals, being one-dimensional, also offer the advantage of reducing the computational complexity compared to multimodal approaches, making them especially attractive for both clinical and computational applications. Most recent approaches leverage these EEG signals through machine learning or deep learning techniques, often reporting standard performance metrics such as accuracy or F1-scores on epoch-based datasets. Many studies have used feature extraction in combination with classical classification algorithms. A notable early study by [PERSON] et al. [20] leveraged the temporal structure of EEG signals, incorporating a hidden Markov model (HMM) to model state transitions in sleep stages. This model was enhanced by extracting features by applying a fast Fourier transform (FFT) using a Hamming window, which led to accuracy of 61.08%. Another study, conducted by [PERSON] and [PERSON] [21], assessed various EEG-derived features for the classification of the sleep stage. Their methodology included temporal features such as the mean value, standard deviation, and Hjorth parameter, along with frequency-based attributes such as the relative spectral power, spectral centroid, and spectral bandwidth. In addition, they incorporated time-frequency characteristics by combining both domains. Using principal component analysis (PCA) as a dimension reduction method, they achieved accuracy of 69.98%. Focusing on efficient classification, [PERSON] et al. [22] proposed a low-computational-cost system designed for implementation in portable sleep monitoring devices. Their method used power spectral density (PSD) features obtained through a short-time Fourier transform (STFT) with a window overlapped in 0.5 s. To optimize feature selection, they applied prototype space feature extraction (PSFE) with fuzzy C-Means (FCM) clustering, alongside reduction methods including linear discriminant analysis (LDA) and PCA. Then, a support vector machine (SVM) was employed in the classification using a radial basis function (RBF) kernel, which produced accuracy of 70.92%. Other researchers have explored alternative classification strategies. [PERSON] et al. [23] focused on EEG-based spectral analysis, extracting features such as cross-frequency coupling (CFC) and spectral power across the delta, theta, alpha, beta, and gamma bands. Their approach used a continuous wavelet transform (CWT) with a Morlet wavelet to analyze phase-amplitude coupling, and classification was performed using LDA with cross-validation, resulting in accuracy of 75%. To further improve the classification accuracy, [PERSON] et al. [24] introduced a novel approach using a Kalman time-varying autoregressive filter (TVAR) for feature extraction. Their classification process was divided into two steps: first, distinguishing between the stages W, REM, and non-REM, followed by refining the classification of the NREM substages (N1, N2, and N3). Using LightGBM and XGBoost classifiers, their system achieved accuracy that exceeded 77% with an average F1-score of 69%. Clustering-based methodologies have also been explored. [PERSON] et al. [25] proposed an improved K-Means clustering algorithm for EEG data segmentation. Unlike traditional K-Means, their method selected cluster centers based on the point density, addressing issues like initialization sensitivity and outliers. The cluster centroids were refined using the \"Three Sigma Rule\" during iterations, leading to classification accuracy of 75% in five sleep stages. [PERSON] [26], who used self-organizing maps (SOM) to create a structured EEG feature space, presented an alternative representation-based technique. This method was complemented by a feature selection process using mutual information (MI) criteria, which identified the most relevant variables for classification.