Unsupervised Sleep and Wake State Identification in Long-Term Electrocorticography Recordings

Studying the neural correlates of sleep can lead to revelations in our understanding of sleep and its interplay with different neurological disorders. Sleep research relies on manual annotation of sleep stages based on rules developed for healthy adults. Automating sleep stage annotation can expedite sleep research and enable us to better understand atypical sleep patterns. Our goal was to create a fully unsupervised approach to label sleep and wake states in human electrocorticography (ECoG) data from epilepsy patients. Here, we demonstrate that with continuous data from a single ECoG electrode, hidden semi-Markov models (HSMM) perform best in classifying sleep/wake states without excessive transitions, with a mean accuracy (n=4) of 85.2% compared to using Kmeans clustering (72.2%) and hidden Markov models (81.5%). Our results confirm that HSMMs produce meaningful labels for ECoG data and establish the groundwork to apply this model to cluster sleep stages and potentially other behavioral states.