Algorithms show accuracy in gauging unconsciousness under GA
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In a new study, a research team shows that a straightforward artificial intelligence approach, attuned to the kind of anesthetic being used, can yield algorithms that assess unconsciousness in patients based on brain activity with high accuracy and reliability.

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.

Researchers applied machine learning approaches to construct classification models for real-time tracking of unconscious state during anesthesia-induced unconsciousness. They 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.

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, with model predictions corresponding with actions taken by the anesthesiologist during the cases. Performance was also strong for 17 patients receiving sevoflurane.

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 the anesthetic state, and this approach may be used to engineer infusion pumps to intelligibly respond to patients’ neural activity.