A probabilistic framework for time-frequency detection of burst suppression

General anesthesia is a drug-induced, reversible condition comprised of hypnosis, amnesia, analgesia, akinesia, and autonomic stability. During the deepest levels of anesthesia, burst suppression is observed in the EEG, which consists of alternating periods of bursting and isoelectric activity. By accurately tracking anesthesia-induced burst suppression, it may be possible to provide a higher level of care for patients receiving general anesthesia. We develop a probabilistic framework for detecting burst suppression events. The algorithm uses multinomial regression to estimate the probability of burst, suppression, and artifact states at each time given EEG frequency-domain data. We test the efficacy of this method on clinical EEG acquired during operating room surgery with GA under propofol.