Sleep Scoring

Sleep scoring, the process of classifying different sleep stages from physiological signals like EEG, aims to automate a time-consuming and subjective task currently performed by human experts. Recent research focuses on improving the accuracy and robustness of automated sleep scoring using deep learning models, including transformers, convolutional neural networks, and recurrent neural networks, often incorporating techniques like contrastive learning and self-supervised learning to enhance feature extraction and generalization across diverse datasets. These advancements hold significant potential for improving the efficiency and accessibility of sleep disorder diagnosis and treatment, particularly through the use of wearable sensors and remote monitoring. Furthermore, research is actively exploring methods to improve the interpretability of these models and address the challenges of inter-rater variability and data heterogeneity.