A clinical operative sequence for hypnosis implementation to general anesthesia during major surgery for orthotopic liver transplantation

Pilia, Eros; Sirigu, Danilo; Mereu, Roberto; Zamboni, Fausto; Pusceddu, Elisabetta

doi:10.1016/j.amsu.2022.104345

Cited by 1 publication

(2 citation statements)

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“…BIS for this purpose is promising [ 36 , 44 ]; however, we suggest that a new approach, which addresses individual correlates of neural activity, may have benefits. It could be used to optimise therapy by controlling depth more precisely when sufficient depth can be helpful [ 24 – 26 , 30 , 32 , 33 , 40 – 43 , 81 ].…”

Section: Methodsmentioning

confidence: 99%

“…Hypnotic “depth” has been a contentious issue, but it is still used in contemporary research to describe the hypnotic state quantitatively [ 5 , 15 – 22 ], alongside the related concepts of “deep hypnosis” [ 6 , 18 , 19 , 23 – 31 ] and “deepening” [ 16 , 32 – 39 ]. Multiple studies indicate that sufficient depth is beneficial in some cases, e.g., in non-pharmacological analgesia [ 24 , 25 , 40 ], general hypnotic anaesthesia [ 26 , 30 , 33 ], etc. Greater depth could result in subjects’ feeling more influenced by hypnotic procedures, leading to better compliance [ 41 , 42 ].…”

Section: Introductionmentioning

confidence: 99%

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Real-time assessment of hypnotic depth, using an EEG-based brain-computer interface: a preliminary study

Obukhov,

Naish,

Solnyshkina

et al. 2023

BMC Res Notes

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Objective Hypnosis can be an effective treatment for many conditions, and there have been attempts to develop instrumental approaches to continuously monitor hypnotic state level (“depth”). However, there is no method that addresses the individual variability of electrophysiological hypnotic correlates. We explore the possibility of using an EEG-based passive brain-computer interface (pBCI) for real-time, individualised estimation of the hypnosis deepening process. Results The wakefulness and deep hypnosis intervals were manually defined and labelled in 27 electroencephalographic (EEG) recordings obtained from eight outpatients after hypnosis sessions. Spectral analysis showed that EEG correlates of deep hypnosis were relatively stable in each patient throughout the treatment but varied between patients. Data from each first session was used to train classification models to continuously assess deep hypnosis probability in subsequent sessions. Models trained using four frequency bands (1.5–45, 1.5–8, 1.5–14, and 4–15 Hz) showed accuracy mostly exceeding 85% in a 10-fold cross-validation. Real-time classification accuracy was also acceptable, so at least one of the four bands yielded results exceeding 74% in any session. The best results averaged across all sessions were obtained using 1.5–14 and 4–15 Hz, with an accuracy of 82%. The revealed issues are also discussed.

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