Data-driven modeling of sleep states from EEG

Sleep analysis is critical for the diagnosis, treatment, and understanding of sleep disorders. However, the current standards for sleep analysis are widely considered oversimplified and problematic. The ability to automatically annotate different states during a night of sleep in a manner that is more descriptive than current standards, as well as the ability to train these models on a patient-by-patient basis, would provide a complementary approach for sleep analysis. We present a method that discovers latent structure in sleep EEG recordings, by extracting symbols from the continuous EEG signal and learning “topics” for a recording. These sleep topics are derived in a fully automatic and data-driven manner, and can represent the data with mixtures of states. The proposed method allows for identification of states in a patient-specific way, as opposed to the one-size-fits-all approach of the current standard. We demonstrate on a publicly available dataset of 15 sleep recordings that not only do the states discovered by this approach encompass the standard sleep stage structure, they provide additional information about sleep architecture with the potential to provide new insights into sleep disorders.