Machine learning of EEG spectra classifies unconsciousness during GABAergic anesthesia.

In current anesthesiology practice, anesthesiologists infer the state of unconsciousness without directly monitoring the brain. Drug- and patient-specific electroencephalographic (EEG) signatures of anesthesia-induced unconsciousness have been identified previously. We applied machine learning approaches to construct classification models for real-time tracking of unconscious state during anesthesia-induced unconsciousness. We used cross-validation to select and train the best performing models using 33,159 2s segments of EEG data recorded from 7 healthy volunteers who received increasing infusions of propofol while responding to stimuli to directly assess unconsciousness. Cross-validated models of unconsciousness performed very well when tested on 13,929 2s EEG segments from 3 left-out volunteers collected under the same conditions (median volunteer AUCs 0.99-0.99). Models showed strong generalization when tested on a cohort of 27 surgical patients receiving solely propofol collected in a separate clinical dataset under different circumstances and using different hardware (median patient AUCs 0.95-0.98), with model predictions corresponding with actions taken by the anesthesiologist during the cases. Performance was also strong for 17 patients receiving sevoflurane (alone or in addition to propofol) (median AUCs 0.88-0.92). These results indicate that EEG spectral features can predict unconsciousness, even when tested on a different anesthetic that acts with a similar neural mechanism. With high performance predictions of unconsciousness, we can accurately monitor anesthetic state, and this approach may be used to engineer infusion pumps to intelligibly respond to patients' neural activity.

Recent advances in modeling sleep: from the clinic to society and disease.

In the past few decades, advances in understanding sleep-wake neurophysiology have occurred hand-in-hand with advances in mathematical modeling of sleep and wake. In this review, we summarize recent updates in modeling the timing and durations of sleep and wake, the underlying neurophysiology of sleep and wake, and the application of these models in understanding cognition and disease. Throughout, we highlight the role modeling has played in developing our understanding of sleep and its underlying mechanisms. We present open questions and controversies in the field and propose the utility of individualized models of sleep for precision sleep medicine.