New algorithms show accuracy, reliability in gauging unconsciousness under general anesthesia

Good news! It is quite surprising that we still know so little about anesthesia and anesthesiology although it is applied daily to thousands and thousands of patients every day around the world.

"Anesthestic drugs act on the brain, but most anesthesiologists rely on heart rate, respiratory rate, and movement to infer whether surgery patients remain unconscious to the desired degree. ...
More than providing a good readout of unconsciousness ... the new algorithms offer the potential to allow anesthesiologists to maintain it at the desired level while using less drug than they might administer when depending on less direct, accurate, and reliable indicators. That can improve patient’s post-operative outcomes, such as delirium. ...
In the new work ... the team trained versions of their AI algorithms, based on different underlying statistical methods, on more than 33,000 2-second-long snippets of EEG recordings from seven of the volunteers. This way the algorithms could “learn” the difference between EEG readings predictive of consciousness and unconsciousness under propofol. ..."

"... We applied machine learning approaches to construct classification models for real-time tracking of unconscious state during anesthesia-induced unconsciousness. ...
These results indicate that EEG spectral features can predict unconsciousness, even when tested on a different anesthetic that acts with a similar neural mechanism. ..." 

New algorithms show accuracy, reliability in gauging unconsciousness under general anesthesia | MIT News | Massachusetts Institute of Technology Machine learning software advances could help anesthesiologists optimize drug dose.

Here is the link to the underlying research article:

Machine learning of EEG spectra classifies unconsciousness during GABAergic anesthesia (open access)