Emergence from general anesthesia and the sleep-manifold

Abstract

The electroencephalogram (EEG) during the re-establishment of consciousness after general anesthesia and surgery varies starkly between patients. Can the EEG during this emergence period provide a means of estimating the underlying biological processes underpinning the return of consciousness? Can we use a model to infer these biological processes from the EEG patterns? A frontal EEG was recorded from 84 patients. Ten patients were chosen for state-space analysis. Five showed archetypal emergences; which consisted of a progressive decrease in alpha power and increase peak alpha frequency before return of responsiveness. The five non-archetypal emergences showed almost no spectral EEG changes (even as the volatile general anesthetic decreased) and then an abrupt return of responsiveness. We used Bayesian methods to estimate the likelihood of an EEG pattern corresponding to the position of the patient on a 2-dimensional manifold in a state space of excitatory connection strength vs. change in intrinsic resting neuronal membrane conductivity. We could thus visualize the trajectory of each patient in the state-space during their emergence period. The patients who followed an archetypal emergence displayed a very consistent pattern; consisting of progressive increase in conductivity, and a temporary period of increased connection strength before return of responsiveness. The non-archetypal emergence trajectories remained fixed in a region of phase space characterized by a relatively high conductivity and low connection strength throughout emergence. This unexpected progressive increase in conductivity during archetypal emergence may be due to an abating of the surgical stimulus during this period. Periods of high connection strength could represent forays into dissociated consciousness, but the model suggests all patients reposition near the fold in the state space to take advantage of bi-stable cortical dynamics before transitioning to consciousness.

Association of electroencephalogram trajectories during emergence from anaesthesia with delirium in the postanaesthesia care unit: an early sign of postoperative complications

“…Any incidence of burst suppression on the intraoperative EEG during maintenance anaesthesia (between surgery start and surgery end) was associated with a 75% increase in odds [1.75 (1.10e2.78)], but burst suppression occurring near induction (initiation of anaesthesia) was not. Further examination of EEG emergence trajectories reveals a relationship between the transitions between anaesthesia states during emergence and PACU-D (see Chander and colleagues 9 and Hight and colleagues, 10 and Supplementary material). Subjects who did not transition through periods of spindle dominance (sdSWA, with pronounced alpha oscillations) had an increased risk of PACU-D (Table 2).…”

“…Previously, we defined EEG patterns during emergence that have been compared with N3, N2, and REM sleep. 9,10 These patterns are termed delta-dominant slow-wave anaesthesia (ddSWA) resembling N3, spindle-dominant SWA (sdSWA) with features similar to N2, and non-SWA (nSWA) resembling REM. Noncanonical patterns of transitions amongst these trajectories through anaesthesia emergence predicted agitation and pain in the recovery room.…”

Emergence EEG pattern classification in sevoflurane anesthesia

“…Different emergence processes may be related to different arousal mechanisms and may provide important insights into the postoperative cognitive dysfunction. Although Hight et al(Hight et al, 2014) attempted to use a sleep-manifold model to describe the mechanisms, the patterns were not categorized in detail and the clinical applications were not considered. Lee et al analyzed the EEG recording of volunteers and classified the anesthesia into two patterns of groups based on the connection strength on information transmission with multi-channel EEG (Lee et al, 2011).…”

“…Lee et al proposed that both continuous and discrete modes existed during LoC and RoC processes (Lee et al, 2011). Hight et al analyzed the clinical EEG recordings and used Bayesian methods to estimate the likelihood of an EEG pattern corresponding to the position of the patient on a 2D manifold in a state space of excitatory connection strength (Hight et al, 2014).…”

“…These measures and indices include the bispectral index (BIS) monitor (Aspect Medical Systems, Newton, MA, USA) (Rampil, 1998), M-entropy module (GE Healthcare, Helsinki, Finland) (Viertiö-Oja et al, 2004), permutation entropy (PE) (Li et al, 2008), approximate entropy (ApEn) (Bruhn et al, 2000), and detrended fluctuation analysis (DFA) (Jospin et al, 2007) etc. It is noteworthy that Hight, et al found that different emergence EEG patterns apparently exhibit different power spectrum characteristics (Hight et al, 2014). They categorized the wake-up period into two patterns, namely, archetypal emergences and non-archetypal emergences, based on the spectral characteristics.…”