Title: Classifying Sleep-Wake Transitions in Rat
Speaker: Linh Huynh (The Ohio State University)
Abstract: Contrary to the common perception that sleep is continuous, sleep is actually fragmented by brief awakenings throughout the night--even in healthy people. Experiments have shown that sleep bouts duration follows an exponential distribution in both infants and adults, while wake bouts duration distribution changes from exponential in infants to power law in adults. To understand this phenomenon of wake bouts and fundamental mechanisms in sleep cycle dynamics, I analyze the transitions between sleep and wake states throughout a night. I apply machine learning methods on rat electromyography data to identify clusters of sleep-wake transitions and compare results with activity of stochastic mathematical models. In this talk, I am going to discuss the background of my research as well as the machine learning methods that I use.