Abstract/Summary

Sleep is a crucial period of rest necessary for optimal cognitive function, psychological well-being, and execution of everyday tasks. In the field of sleep healthcare, the primary objective is to identify and classify the various sleep states. Implementing sleep state detection in a system is problematic and essential for accurate diagnosis. Our study used an integrated framework to recognize sleep states. The dataset contained approximately eight lakh data points sorted into two groups: onset and wake-up. We successfully deployed a cutting-edge Convolutional Recurrent Transformer (CRT) model for sleep state detection. The training accuracy of our detection model was measured at 97.83%, a constant validation accuracy of 97.07%, and a testing measurement accuracy of 97.23%, were maintained. These scores indicate the model’s proficiency in precisely recognizing the sleep states. Our system’s detection capabilities demonstrate the ability to identify different sleep states, enhance the accuracy of diagnoses and increase healthcare outcomes in this specialized field.