Studying the Sleep Dynamics in Healthy and disordered Humans

I am actively involved in studying sleep dynamics using machine learning methods. Our goal is to understand different sleep stages through the extracted features. We have found evidence of sleep stage 2 duality and sleep stage inertia. This project is funded by the China Postdoctoral Science Foundation.

Recently, we published our results for seizure detection using the transient variant of the Deep Boltzmann machine. We extended a similar approach for binary classification of sleep stages. Please check my GitHub for the codes.