# Hybrid Deep Learning Approach for Automated Sleep Cycle Analysis
## 2 Related Works
Using an SVM with an RBF kernel and hyperparameter tuning via cross-validation, this approach yielded a classification accuracy score of 70.43%. Recent advances in deep learning have significantly improved the sleep stage classification performance. Unlike traditional methods that rely on manually crafted features, deep learning models can automatically extract relevant patterns from raw EEG signals [27]. One of the first CNN-based approaches was introduced by [PERSON] et al. [5], who designed a convolutional neural network for the classification of sleep stages using single-channel EEG data. Their architecture consisted of a pair of convolutional layers that incorporated a pooling function, connected to dense layers, and a softmax classifier. They employed rectified linear activation functions and used a cross-entropy loss criterion. The EEG signals were preprocessed using wavelet analysis, and class balancing was achieved through stochastic gradient descent with weighted sampling. Their model achieved accuracy of 74%. More advanced CNN architectures have been developed since then. [PERSON] et al. [7] proposed a CNN model that classified 30 s EEG epochs while incorporating contextual information from neighboring epochs. The training process used the ADAM optimizer with a data distribution of training (50%), validation (20%), and testing (30%). Their system demonstrated high accuracy of 87%. Hybrid deep learning models have also gained traction in sleep stage classification. [PERSON] et al. [28] developed a nine-layer CNN-BiLSTM architecture that combined convolutional layers for feature extraction, BiLSTM layers for temporal modeling, and residual connections. Given the unbalanced distribution of data between categories of sleep, they implemented adaptive learning rate adjustments to avoid overfitting in the CNN layers. In addition, gradient clipping was applied in the BiLSTM layers to stabilize the training. The model achieved accuracy of 84.1%. Another hybrid model, SleepEEGNet, was proposed by [PERSON] et al. [29]. The architecture combined CNN layers with BiLSTM layers that functioned as both encoders and decoders. Their system reached 84.26% precision, an F1-score of 79.66%, and a [PERSON]'s kappa coefficient of 0.79. Similarly, [PERSON] et al. [30] introduced a multichannel CNN-BiLSTM model that integrated EEG (Fpz-Cz, Pz-Oz), ECG, and EMG signals. Their two-stage design leveraged pretrained dual-channel modules combined via transfer learning. The architecture reported accuracy of 91.44% with an F1-score of 88.69% using the Sleep-EDF 20 dataset, as well as accuracy of 90.21% over the extended Sleep-EDF 78 version. Despite these improvements, certain challenges remain in classifying sleep stages, including the difficulty in accurately identifying stage N1, which is often underrepresented in datasets due to its short duration within the sleep cycle, and issues related to class imbalance. However, the ongoing development of deep learning methodologies and feature engineering strategies has contributed to increased classification accuracy. The use of models of hybrid neural networks and advanced preprocessing techniques suggests promising directions for the construction of improved models for the detection of sleep stages.