Journal of Engineering and Applied Sciences Technology

In this paper we develop and evaluates Convolutional Neural Network (CNN) models for automatic sleep stage segmentation using single-channel EEG signals from the Sleep-EDF dataset. We explored three architectures: CNN-CNN, CNN-CNN-Conditional Random Field (CRF), and CNN-Long Short-Term Memory (LSTM). The CNN-CNN-CRF model, incorporating a CRF layer for sequence labeling, demonstrated the highest performance with an accuracy of 89% and an F1 score of 82%, outperforming CNN-CNN (accuracy 87%, F1 score 81%) and CNN-LSTM (accuracy 71%, F1 score 76%). This approach surpasses existing state-of-the-art models by effectively capturing temporal dependencies between sleep epochs and ensuring consistent sequence labeling. The findings suggest that integrating CRFs with CNNs enhances classification performance, providing a robust solution for automated sleep stage segmentation. These results highlight the potential of deep learning in improving sleep analysis, with implications for sleep quality assessment and clinical applications.