Sleep Study

Sleep studies are increasingly leveraging machine learning to automate sleep stage classification and sleep disorder detection, moving beyond traditional polysomnography (PSG) methods. Current research focuses on developing robust algorithms, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and other deep learning architectures, to analyze diverse data sources such as EEG, actigraphy, and even video recordings of vital signs. This work aims to improve diagnostic accuracy, reduce the cost and invasiveness of sleep assessments, and ultimately enhance personalized sleep health management. The development of more efficient and accurate automated methods holds significant promise for improving the diagnosis and treatment of sleep disorders.