[The quantitative EEG in electroencephalogram-based brain monitoring during general anesthesia]

The electroencephalogram (EEG) is increasingly being used in the clinical routine of anesthesia in German-speaking countries. In over 90% of patients the frontal EEG changes somewhat predictably in response to administration of the normally used anesthetic agents (propofol and volatile gasses). An adequate depth of anesthesia and appropriate concentrations of anesthetics in the brain generate mostly frontal oscillations between 8 and 12 Hz as well as slow delta waves between 0.5 and 4 Hz. The frontal EEG channel is well-suited for avoidance of insufficient depth of anesthesia and excessive administration of anesthetics. This article explains the clinical interpretation of the most important EEG patterns and the biophysical background. Also discussed are important limitations and pitfalls for the clinical routine, which the anesthetist should know in order to utilize the EEG as an admittedly incomplete but clinically extremely important parameter for the level of consciousness.

Effects of noxious stimulation on the electroencephalogram during general anaesthesia: a narrative review and approach to analgesic titration

Electroencephalographic (EEG) activity is used to monitor the neurophysiology of the brain, which is a target organ of general anaesthesia. Besides its use in evaluating hypnotic states, neurophysiologic reactions to noxious stimulation can also be observed in the EEG. Recognising and understanding these responses could help optimise intraoperative analgesic management. This review describes three types of changes in the EEG induced by noxious stimulation when the patient is under general anaesthesia: (1) beta arousal, (2) (paradoxical) delta arousal, and (3) alpha dropout. Beta arousal is an increase in EEG power in the beta-frequency band (12-25 Hz) in response to noxious stimulation, especially at lower doses of anaesthesia drugs in the absence of opioids. It is usually indicative of a cortical depolarisation and increased cortical activity. At higher concentrations of anaesthetic drug, and with insufficient opioids, delta arousal (increased power in the delta band [0.5-4 Hz]) and alpha dropout (decreased alpha power [8-12 Hz]) are associated with noxious stimuli. The mechanisms of delta arousal are not well understood, but the midbrain reticular formation seems to play a role. Alpha dropout may indicate a return of thalamocortical communication, from an idling mode to an operational mode. Each of these EEG changes reflect an incomplete modulation of pain signals and can be mitigated by administration of opioid or the use of regional anaesthesia techniques. Future studies should evaluate whether titrating analgesic drugs in response to these EEG signals reduces postoperative pain and influences other postoperative outcomes, including the potential development of chronic pain.

Continuing professional development module : An updated introduction to electroencephalogram-based brain monitoring during intended general anesthesia

The electroencephalogram (EEG) provides a reliable reflection of the brain's electrical state, so it can reassure us that the anesthetic agents are actually reaching the patient's brain, and are having the desired effect. In most patients, the EEG changes somewhat predictably in response to propofol and volatile agents, so a frontal EEG channel can guide avoidance of insufficient and excessive administration of general anesthesia. Persistent alpha-spindles (around 10 Hz) phase-amplitude coupled with slow delta waves (around 1 Hz) are commonly seen during an "appropriate hypnotic state of general anesthesia". Such patterns can be appreciated from the EEG waveform or from the spectrogram (a colour-coded display of how the power in the various EEG frequencies changes with time). Nevertheless, there are exceptions to this. For example, administration of ketamine and nitrous oxide is generally not associated with the aforementioned alpha-spindle coupled with delta wave pattern. Also, some patients, including older adults and those with neurodegenerative disorders, are less predisposed to generate a strong electroencephalographic "alpha-spindle" pattern during general anesthesia. There might also be some rare instances when the frontal EEG shows a pattern suggestive of general anesthesia, while the patient has some awareness and is able to follow simple commands, albeit this is typically without obvious distress or memory formation. Thus, the frontal EEG alone, as currently analyzed, is an imperfect but clinically useful mirror, and more scientific insights will be needed before we can claim to have a reliable readout of brain "function" during general anesthesia.

Low Frontal Alpha Power Is Associated With the Propensity for Burst Suppression: An Electroencephalogram Phenotype for a "Vulnerable Brain"

BACKGROUND

A number of recent studies have reported an association between intraoperative burst suppression and postoperative delirium. These studies suggest that anesthesia-induced burst suppression may be an indicator of underlying brain vulnerability. A prominent feature of electroencephalogram (EEG) under propofol and sevoflurane anesthesia is the frontal alpha oscillation. This frontal alpha oscillation is known to decline significantly during aging and is generated by prefrontal brain regions that are particularly prone to age-related neurodegeneration. Given that burst suppression and frontal alpha oscillations are both associated with brain vulnerability, we hypothesized that anesthesia-induced frontal alpha power could also be associated with burst suppression.

METHODS

We analyzed EEG data from a previously reported cohort in which 155 patients received propofol (n = 60) or sevoflurane (n = 95) as the primary anesthetic. We computed the EEG spectrum during stable anesthetic maintenance and identified whether or not burst suppression occurred during the anesthetic. We characterized the relationship between burst suppression and alpha power using logistic regression. We proposed 5 different models consisting of different combinations of potential contributing factors associated with burst suppression: (1) a Base Model consisting of alpha power; (2) an Extended Mechanistic Model consisting of alpha power, age, and drug dosing information; (3) a Clinical Confounding Factors Model consisting of alpha power, hypotension, and other confounds; (4) a Simplified Model consisting only of alpha power and propofol bolus administration; and (5) a Full Model consisting of all of these variables to control for as much confounding as possible.

RESULTS

All models show a consistent significant association between alpha power and burst suppression while adjusting for different sets of covariates, all with consistent effect size estimates. Using the Simplified Model, we found that for each decibel decrease in alpha power, the odds of experiencing burst suppression increased by 1.33-fold.

CONCLUSIONS

In this study, we show how a decrease in anesthesia-induced frontal alpha power is associated with an increased propensity for burst suppression, in a manner that captures individualized information above and beyond a patient's chronological age. Lower frontal alpha band power is strongly associated with higher propensity for burst suppression and, therefore, potentially higher risk of postoperative neurocognitive disorders. We hypothesize that low frontal alpha power and increased propensity for burst suppression together characterize a "vulnerable brain" phenotype under anesthesia that could be mechanistically linked to brain metabolism, cognition, and brain aging.

Variations in Electrocortical Activity due to Surgical Incision in Anaesthetized Cardiac Patients: Electroencephalogram-Based Quantitative Analysis

This study examines the alterations in scalp recorded cortical activity due to surgical incision in anaesthetized cardiac patients using electroencephalogram (EEG) patterns. The primary aim was to compare the changes in electrocortical activity after surgical incision. The secondary aim was to compare the changes in time, frequency, and wavelet domain parameters after loss of consciousness (LoC) and after intubation. Real-time EEG data were recorded from 19 patients undergoing cardiac surgery and signals were quantified with time, frequency, and wavelet domain parameters. An increase in hjorth activity, hjorth complexity, rms value, total band power, relative delta band power, standard deviation and maxima of approximation coefficients (a₅), minima of detail coefficients (d₅, d₄, and d₃) and decrease in hjorth mobility; approximate entropy; relative theta, alpha, and beta band power; specentropy; median, spectral edge, and mean frequency; mean of detail coefficients (d₄); standard deviation of detail coefficients (d₅, d₄, and d₃); maxima of detail coefficients (d₅); and minima of approximation coefficients (a₅) were observed during LoC. Decrease in hjorth activity; hjorth mobility; rms value; total band power; relative theta band power; median frequency; standard deviation of coefficients (a₅, d₅, d₄, and d₃); and maxima of coefficients (a₅, d₅, d₄, and d₃) and increase in hjorth complexity; mean of detail coefficients (d₅); and minima of coefficients (a₅, d₅, d₄, and d₃) were observed after intubation. Significant decrease in hjorth activity; hjorth mobility; total band power; relative alpha band power; specentropy; median and mean frequency; standard deviation and maxima of detail coefficients (d₅, d₄, and d₃) and increase in rms value; relative delta band power; mean of coefficients (a₅ and d₅); and minima of coefficients (d₅, d₄, and d₃) were observed due to surgical incision. It can be concluded that different spectral and temporal parameters of EEG signals are highly sensitive to induction, intubation, and surgical incision which are potentially informative for measuring the depth of anaesthesia or efficacy of anaesthetic agents.

Modulation of frontal EEG alpha oscillations during maintenance and emergence phases of general anaesthesia to improve early neurocognitive recovery in older patients: protocol for a randomised controlled trial

BACKGROUND

Postoperative delirium may manifest in the immediate post-anaesthesia care period. Such episodes appear to be predictive of further episodes of inpatient delirium and associated adverse outcomes. Frontal electroencephalogram (EEG) findings of suppression patterns and low proprietary index values have been associated with postoperative delirium and poor outcomes. However, the efficacy of titrating anaesthesia to proprietary index targets for preventing delirium remains contentious. We aim to assess the efficacy of two strategies which we hypothesise could prevent post-anaesthesia care unit (PACU) delirium by maximising the alpha oscillation observed in frontal EEG channels during the maintenance and emergence phases of anaesthesia.

METHODS

This is a 2 × 2 factorial, double-blind, stratified, randomised control trial of 600 patients. Eligible patients are those aged 60 years or over who are undergoing non-cardiac, non-intracranial, volatile-based anaesthesia of expected duration of more than 2 h. Patients will be stratified by pre-operative cognitive status, surgery type and site. For the maintenance phase of anaesthesia, patients will be randomised (1:1) to an alpha power-maximisation anaesthesia titration strategy versus standard care avoiding suppression patterns in the EEG. For the emergence phase of anaesthesia, patients will be randomised (1:1) to early cessation of volatile anaesthesia and emergence from an intravenous infusion of propofol versus standard emergence from volatile anaesthesia only. The primary study outcomes are the power of the frontal alpha oscillation during the maintenance and emergence phases of anaesthesia. Our main clinical outcome of interest is PACU delirium.

DISCUSSION

This is a largely exploratory study; the extent to which EEG signatures can be modified by titration of pharmacological agents is not known. The underlying concept is maximisation of anaesthetic efficacy by individualised drug titration to a clearly defined EEG feature. The interventions are already clinically used strategies in anaesthetic practice, but have not been formally evaluated. The addition of propofol during the emergence phase of volatile-based general anaesthesia is known to reduce emergence delirium in children; however, the efficacy of this strategy in older patients is not known.

TRIAL REGISTRATION

Australian and New Zealand Clinical Trial Registry, ID: 12617001354370 . Registered on 27/09/2017.

Changes in Alpha Frequency and Power of the Electroencephalogram during Volatile-Based General Anesthesia

Oscillations in the electroencephalogram (EEG) at the alpha frequency (8–12 Hz) are thought to be ubiquitous during surgical anesthesia, but the details of how this oscillation responds to ongoing changes in volatile anesthetic concentration have not been well characterized. It is not known how often alpha oscillations are absent in the clinical context, how sensitively alpha frequency and power respond to changes in anesthetic concentration, and what effect increased age has on alpha frequency. Bipolar EEG was recorded frontally from 305 patients undergoing surgery with sevoflurane or desflurane providing general anesthesia. A new method of detecting the presence of alpha oscillations based on the stability of the rate of change of the peak frequency in the alpha range was developed. Linear concentration-response curves were fitted to assess the sensitivity of alpha power and frequency measures to changing levels of anesthesia. Alpha oscillations were seen to be inexplicably absent in around 4% of patients. Maximal alpha power increased with increasing volatile anesthetic concentrations in half of the patients, and decreased in the remaining patients. Alpha frequency decreased with increasing anesthetic concentrations in near to 90% of patients. Increasing age was associated with decreased sensitivity to volatile anesthesia concentrations, and with decreased alpha frequency, which sometimes transitioned into the theta range (5–7 Hz). While peak alpha frequency shows a consistent slowing to increasing volatile concentrations, the peak power of the oscillation does not, suggesting that frequency might be more informative of depth of anesthesia than traditional power based measures during volatile-based anesthesia. The alpha oscillation becomes slower with increasing age, even when the decreased anesthetic needs of older patients were taken into account.

[Drug-induced sedation endoscopy-quo vadis? : Review and outlook]

BACKGROUND

Drug-induced sedation endoscopy (DISE) is a diagnostic procedure which allows evaluation of the collapsibility of the upper airway. According to expert opinion, it is possible to imitate nocturnal collapsibility and perform a realistic investigation of the site of obstruction and vibration. This should enable sufficient and precise therapeutic advice to be given solely on the basis of clinical assessment.

OBJECTIVE

The current publication critically evaluates the present state of development of DISE and its potential indications.

MATERIALS AND METHODS

A PubMed literature research was performed using "sleep" and "endoscopy" or "DISE" as keywords. Relevant publications were evaluated.

RESULTS

The present publication provides a historical summary of the available publications and relates these to other methods for examining obstructive sleep apnea. The present state of DISE in terms of drugs applied, grading systems, and validity is evaluated. Indications for DISE are described and critically discussed on the basis of literature data.

CONCLUSION

DISE provides deep insights into the genesis of obstructions of the upper airway and snoring. Although its value for diagnosis and treatment of sleep-disordered breathing could not yet be demonstrated for all non-CPAP (continuous positive airway pressure) therapies, DISE could identify predictive parameters some methods. Further potential indications for DISE might be predictive examinations for mandibular advancement devices and respiration-synchronous neurostimulation of the hypoglossal nerve. DISE will thus remain a valuable diagnostic tool for obstructive sleep apnea and rhonchopathy.

Characterizing Awake and Anesthetized States Using a Dimensionality Reduction Method

Distinguishing between awake and anesthetized states is one of the important problems in surgery. Vital signals contain valuable information that can be used in prediction of different levels of anesthesia. Some monitors based on electroencephalogram (EEG) such as the Bispectral (BIS) index have been proposed in recent years. This study proposes a new method for characterizing between awake and anesthetized states. We validated our method by obtaining data from 25 patients during the cardiac surgery that requires cardiopulmonary bypass. At first, some linear and non-linear features are extracted from EEG signals. Then a method called "LLE"(Locally Linear Embedding) is used to map high-dimensional features in a three-dimensional output space. Finally, low dimensional data are used as an input to a quadratic discriminant analyzer (QDA). The experimental results indicate that an overall accuracy of 88.4 % can be obtained using this method for classifying the EEG signal into conscious and unconscious states for all patients. Considering the reliability of this method, we can develop a new EEG monitoring system that could assist the anesthesiologists to estimate the depth of anesthesia accurately.

Metric learning for automatic sleep stage classification

We introduce in this paper a metric learning approach for automatic sleep stage classification based on single-channel EEG data. We show that learning a global metric from training data instead of using the default Euclidean metric, the k-nearest neighbor classification rule outperforms state-of-the-art methods on Sleep-EDF dataset with various classification settings. The overall accuracy for Awake/Sleep and 4-class classification setting are 98.32% and 94.49% respectively. Furthermore, the superior accuracy is achieved by performing classification on a low-dimensional feature space derived from time and frequency domains and without the need for artifact removal as a preprocessing step.

Electroencephalographic variation during end maintenance and emergence from surgical anesthesia

The re-establishment of conscious awareness after discontinuing general anesthesia has often been assumed to be the inverse of loss of consciousness. This is despite the obvious asymmetry in the initiation and termination of natural sleep. In order to characterize the restoration of consciousness after surgery, we recorded frontal electroencephalograph (EEG) from 100 patients in the operating room during maintenance and emergence from general anesthesia. We have defined, for the first time, 4 steady-state patterns of anesthetic maintenance based on the relative EEG power in the slow-wave (<14 Hz) frequency bands that dominate sleep and anesthesia. Unlike single-drug experiments performed in healthy volunteers, we found that surgical patients exhibited greater electroencephalographic heterogeneity while re-establishing conscious awareness after drug discontinuation. Moreover, these emergence patterns could be broadly grouped according to the duration and rapidity of transitions amongst these slow-wave dominated brain states that precede awakening. Most patients progressed gradually from a pattern characterized by strong peaks of delta (0.5-4 Hz) and alpha/spindle (8-14 Hz) power ('Slow-Wave Anesthesia') to a state marked by low delta-spindle power ('Non Slow-Wave Anesthesia') before awakening. However, 31% of patients transitioned abruptly from Slow-Wave Anesthesia to waking; they were also more likely to express pain in the post-operative period. Our results, based on sleep-staging classification, provide the first systematized nomenclature for tracking brain states under general anesthesia from maintenance to emergence, and suggest that these transitions may correlate with post-operative outcomes such as pain.

A probabilistic framework for a physiological representation of dynamically evolving sleep state

This work presents a probabilistic method for mapping human sleep electroencephalogram (EEG) signals onto a state space based on a biologically plausible mathematical model of the cortex. From a noninvasive EEG signal, this method produces physiologically meaningful pathways of the cortical state over a night of sleep. We propose ways in which these pathways offer insights into sleep-related conditions, functions, and complex pathologies. To address explicitly the noisiness of the EEG signal and the stochastic nature of the mathematical model, we use a probabilistic Bayesian framework to map each EEG epoch to a distribution of likelihoods over all model sleep states. We show that the mapping produced from human data robustly separates rapid eye movement sleep (REM) from slow wave sleep (SWS). A Hidden Markov Model (HMM) is incorporated to improve the path results using the prior knowledge that cortical physiology has temporal continuity.

Slow-wave activity saturation and thalamocortical isolation during propofol anesthesia in humans

The altered state of consciousness produced by general anesthetics is associated with a variety of changes in the brain's electrical activity. Under hyperpolarizing influences such as anesthetic drugs, cortical neurons oscillate at ~1 Hz, which is measurable as slow waves in the electroencephalogram (EEG). We have administered propofol anesthesia to 16 subjects and found that, after they had lost behavioral responsiveness (response to standard sensory stimuli), each individual's EEG slow-wave activity (SWA) rose to saturation and then remained constant despite increasing drug concentrations. We then simultaneously collected functional magnetic resonance imaging and EEG data in 12 of these subjects during propofol administration and sensory stimulation. During the transition to SWA saturation, the thalamocortical system became isolated from sensory stimuli, whereas internal thalamocortical exchange persisted. Rather, an alternative and more fundamental cortical network (which includes the precuneus) responded to all sensory stimulation. We conclude that SWA saturation is a potential individualized indicator of perception loss that could prove useful for monitoring depth of anesthesia and studying altered states of consciousness.

Interfaces of sleep and anesthesia

In the past decades there has been an increasing focus on the relationship of sleep and anesthesia. This relationship bears on the fundamental scientific questions in anesthesiology, such as the mechanism of anesthetic-induced unconsciousness. However, given the increasing prevalence of sleep disorders in surgical patients, the interfaces of sleep and anesthesia are now a pressing clinical concern. This article discusses sleep and anesthesia from the perspective of phenotype, mechanism and function, with some concluding thoughts on the relevance to neuroanesthesiology.

Unresponsiveness ≠ unconsciousness

Consciousness is subjective experience. During both sleep and anesthesia, consciousness is common, evidenced by dreaming. A defining feature of dreaming is that, while conscious, we do not experience our environment; we are disconnected. Besides inducing behavioral unresponsiveness, a key goal of anesthesia is to prevent the experience of surgery (connected consciousness), by inducing either unconsciousness or disconnection of consciousness from the environment. Review of the isolated forearm technique demonstrates that consciousness, connectedness, and responsiveness uncouple during anesthesia; in clinical conditions, a median 37% of patients demonstrate connected consciousness. We describe potential neurobiological constructs that can explain this phenomenon: during light anesthesia the subcortical mechanisms subserving spontaneous behavioral responsiveness are disabled but information integration within the corticothalamic network continues to produce consciousness, and unperturbed norepinephrinergic signaling maintains connectedness. These concepts emphasize the need for developing anesthetic regimens and depth of anesthesia monitors that specifically target mechanisms of consciousness, connectedness, and responsiveness.

The Association between Intraoperative Electroencephalogram-Based Measures and Pain Severity in the Post-Anaesthesia Care Unit

This observational study aimed to identify simple electroencephalogram indices of inadequate intraoperative opioid-mediated nociceptive blockade and to compare these indices with routinely used clinical predictors of severe postoperative pain in adults. Intraoperative trend and waveform data (electrocardiogram, pulse oximetry and electroencephalogram) were collected, pain intensity in the post-anaesthesia care unit was quantified using an 11-point Verbal Rating Score, and opioid administration was recorded. Using the initial post-anaesthesia care unit Verbal Rating Score as the primary endpoint, the relationship between five possible explanatory variables – surgery type, depth of volatile anaesthesia (minimum alveolar concentration), electroencephalogram signs (state entropy, spindle-like activity and delta-band power) and estimated end-of-operation effect-site morphine concentrations – was examined. One hundred and thirteen patients were recruited, with 94 included in the final clinical and electroencephalogram data analysis. Fifty-two patients had moderate or severe pain (Verbal Rating Score >5). State entropy was lower (46.5±2.9 vs 43.1±1.9, P=0.04) and spindle-like activity higher (0.42±0.03 vs 0.50±0.02, P=0.03) in the moderate/severe pain group. These findings suggest that there is a modest association between electroencephalogram measures near the end of surgery and the severity of postoperative pain.

Electroencephalogram monitoring during intracranial surgery for moyamoya disease

We describe our experience with intraoperative electroencephalography in moyamoya surgery, a method to monitor for ischemic changes during the procedure and to minimize the risk of intraoperative and perioperative stroke. Case records and intraoperative electroencephalography recordings of all patients (n=220) treated with surgical revascularization for moyamoya (pial synangiosis) performed for 14 years (1994-2008) were reviewed. Electroencephalographic slowing occurred in 100 cases (45.5%), and was persistent in nine cases (9%). Slowing coincided with specific operative manipulations, most commonly while suturing the donor vessel to the pia, and during closure of the craniotomy. Slowing generally occurred bilaterally, independently of the side of intervention. The presence, length, and severity of slowing were not predictive of perioperative ischemic events. We present additional data on intraoperative electroencephalography with a modified montage to accommodate the craniotomy. Although not predictive of perioperative ischemic events in this series, electroencephalographic changes were correlated with specific operative interventions, and revealed global responses to unilateral manipulation. These findings suggest that prospective analyses of this technique may elucidate additional methods of predicting (and possibly preventing) perioperative ischemic events.

A continuous mapping of sleep states through association of EEG with a mesoscale cortical model

Here we show that a mathematical model of the human sleep cycle can be used to obtain a detailed description of electroencephalogram (EEG) sleep stages, and we discuss how this analysis may aid in the prediction and prevention of seizures during sleep. The association between EEG data and the cortical model is found via locally linear embedding (LLE), a method of dimensionality reduction. We first show that LLE can distinguish between traditional sleep stages when applied to EEG data. It reliably separates REM and non-REM sleep and maps the EEG data to a low-dimensional output space where the sleep state changes smoothly over time. We also incorporate the concept of strongly connected components and use this as a method of automatic outlier rejection for EEG data. Then, by using LLE on a hybrid data set containing both sleep EEG and signals generated from the mesoscale cortical model, we quantify the relationship between the data and the mathematical model. This enables us to take any sample of sleep EEG data and associate it with a position among the continuous range of sleep states provided by the model; we can thus infer a trajectory of states as the subject sleeps. Lastly, we show that this method gives consistent results for various subjects over a full night of sleep and can be done in real time.