Substance-Specific Differences in Human Electroencephalographic Burst Suppression Patterns

GABAergic Neurons in the Central Amygdala Promote Emergence from Isoflurane Anesthesia in Mice

BACKGROUND

Recent evidence indicates that general anesthesia and sleep-wake behavior share some overlapping neural substrates. γ-Aminobutyric acid-mediated (GABAergic) neurons in the central amygdala have a high firing rate during wakefulness and play a role in regulating arousal-related behaviors. The objective of this study was to investigate whether central amygdala GABAergic neurons participate in the regulation of isoflurane general anesthesia and uncover the underlying neural circuitry.

METHODS

Fiber photometry recording was used to determine the changes in calcium signals of central amygdala GABAergic neurons during isoflurane anesthesia in Vgat-Cre mice. Chemogenetic and optogenetic approaches were used to manipulate the activity of central amygdala GABAergic neurons, and a righting reflex test was used to determine the induction and emergence from isoflurane anesthesia. Cortical electroencephalogram (EEG) recording was used to assess the changes in EEG spectral power and burst-suppression ratio during 0.8% and 1.4% isoflurane anesthesia, respectively. Both male and female mice were used in this study.

RESULTS

The calcium signals of central amygdala GABAergic neurons decreased during the induction of isoflurane anesthesia and were restored during the emergence. Chemogenetic activation of central amygdala GABAergic neurons delayed induction time (mean ± SD, vehicle vs . clozapine-N-oxide: 58.75 ± 5.42 s vs . 67.63 ± 5.01 s; n = 8; P = 0.0017) and shortened emergence time (385.50 ± 66.26 s vs . 214.60 ± 40.21 s; n = 8; P = 0.0017) from isoflurane anesthesia. Optogenetic activation of central amygdala GABAergic neurons produced a similar effect. Furthermore, optogenetic activation decreased EEG delta power (prestimulation vs . stimulation: 46.63 ± 4.40% vs . 34.16 ± 6.47%; n = 8; P = 0.0195) and burst-suppression ratio (83.39 ± 5.15% vs . 52.60 ± 12.98%; n = 8; P = 0.0003). Moreover, optogenetic stimulation of terminals of central amygdala GABAergic neurons in the basal forebrain also promoted cortical activation and accelerated behavioral emergence from isoflurane anesthesia.

CONCLUSIONS

The results suggest that central amygdala GABAergic neurons play a role in general anesthesia regulation, which facilitates behavioral and cortical emergence from isoflurane anesthesia through the GABAergic central amygdala-basal forebrain pathway.

Electroencephalographic depression after abruptly increasing partial pressure of end-tidal carbon dioxide: a case series

BACKGROUND

Prolonged electroencephalographic depression during surgery is associated with poor outcomes for patients. However, the published literature on electroencephalographic depression caused by a sudden increase in the partial pressure of end-tidal carbon dioxide (PETCO2) is lacking.

CASE PRESENTATION

We report four patients who were scheduled for laparoscopic liver surgery under general anesthesia. During the process of EEG monitoring with Sedline, four patients experienced electroencephalographic depression closely after a sudden increase in PETCO2. The four patients showed that electroencephalographic depression mainly manifested as a slow in EEG frequency, a reduction in the amplitude and power of EEG, and a decrease in spectral edge frequency. Patient state index was elevated in three cases.

CONCLUSIONS

To summarize, our patients showed EEG depression when PETCO2 suddenly increased, which suggests that clinical doctors should be alert to electroencephalographic depression when the PETCO2 abruptly increases. EEG monitoring devices should be applied in patients with possible hypercapnia. Anesthesiologists must comprehensively interpret the raw EEG, spectral edge frequency, and density spectral array data, in addition to patient sedation index values.

Index of Consciousness monitoring may effectively predict and prevent circulatory stress induced by endotracheal intubation under general anesthesia: a prospective randomized controlled trial

BACKGROUND

The primary objective of anesthesiologists during the induction of anaesthesia is to mitigate the operative stress response resulting from endotracheal intubation. In this prospective, randomized controlled trial, our aim was to assess the feasibility and efficacy of employing Index of Consciousness (IoC, IoC1 and IoC2) monitoring in predicting and mitigating circulatory stress induced by endotracheal intubation for laparoscopic cholecystectomy patients under general anesthesia (GA).

METHODS

We enrolled one hundred and twenty patients scheduled for laparoscopic cholecystectomy under GA and randomly allocated them to two groups: IoC monitoring guidance (Group T, n = 60) and bispectral index (BIS) monitoring guidance (Group C, n = 60). The primary endpoints included the heart rate (HR) and mean arterial pressure (MAP) of the patients, as well as the rate of change (ROC) at specific time points during the endotracheal intubation period. Secondary outcomes encompassed the systemic vascular resistance index (SVRI), cardiac output index (CI), stroke volume index (SVI), ROC at specific time points, the incidence of adverse events (AEs), and the induction dosage of remifentanil and propofol during the endotracheal intubation period in both groups.

RESULTS

The mean (SD) HR at 1 min after intubation under IoC monitoring guidance was significantly lower than that under BIS monitoring guidance (76 (16) beats/min vs. 82 (16) beats/min, P = 0.049, respectively). Similarly, the mean (SD) MAP at 1 min after intubation under IoC monitoring guidance was lower than that under BIS monitoring guidance (90 (20) mmHg vs. 98 (19) mmHg, P = 0.031, respectively). At each time point from 1 to 5 min after intubation, the number of cases with HR ROC of less than 10% in Group T was significantly higher than in Group C (P < 0.05). Furthermore, between 1 and 3 min and at 5 min post-intubation, the number of cases with HR ROC between 20 to 30% or 40% in Group T was significantly lower than that in Group C (P < 0.05). At 1 min post-intubation, the number of cases with MAP ROC of less than 10% in Group T was significantly higher than that in Group C (P < 0.05), and the number of cases with MAP ROC between 10 to 20% in Group T was significantly lower than that in Group C (P < 0.01). Patients in Group T exhibited superior hemodynamic stability during the peri-endotracheal intubation period compared to those in Group C. There were no significant differences in the frequencies of AEs between the two groups (P > 0.05).

CONCLUSION

This promising monitoring technique has the potential to predict the circulatory stress response, thereby reducing the incidence of adverse reactions during the peri-endotracheal intubation period. This technology holds promise for optimizing anesthesia management.

TRAIL REGISTRATION

Chinese Clinical Trail Registry Identifier: ChiCTR2300070237 (20/04/2022).

Quantitative electroencephalography predicts postoperative delirium in cardiac surgical patients after cardiopulmonary bypass: a prospective observational study

Objective

Despite its frequency and associated negative effect, delirium remains poorly recognized in postoperative patients after ICU admission, especially among those who have undergone cardiac surgery with cardiopulmonary bypass. Postoperative delirium is triggered by a wide variety of acute medical conditions associated with impaired neuronal network connectivity. The lack of objective biomarkers primarily hinders the early detection of delirium. Seeking early biomarkers for tracking POD could potentially assist in predicting the onset of delirium and assessing the severity of delirium and response to interventions.

Methods

QEEGs were taken from 46 sedated postoperative patients, with 24 of them having undergone cardiac surgery. The assessment of delirium was performed twice daily using the Confusion Assessment Method for the ICU (CAM-ICU) to screen for postoperative delirium (POD). QEEG data were interpreted clinically by neurophysiologists and processed by open-source EEGLAB to identify features in patients who had or did not have POD after cardiac or non-cardiac surgery.

Results

The incidence of delirium in patients after undergoing cardiac surgery was nine times greater than in those after non-cardiac surgeries (41.7% vs. 4.5%; p = 0.0046). Patients with delirium experienced longer use of mechanical ventilation (118 h (78,323) compared to 20 h (18,23); p < 0.0001) and an extended ICU length of stay (7 days (6, 20) vs. 2 days (2, 4); p < 0.0001). The depth of anesthesia, as measured by RASS scores (p = 0.3114) and spectral entropy (p = 0.1504), showed no significant difference. However, notable differences were observed between delirious and non-delirious patients in terms of the amplitude-integrated EEG (aEEG) upper limit, the relative power of the delta band, and spectral edge frequency 95 (SEF95) (p = 0.0464, p = 0.0417, p = 0.0337, respectively).

Conclusion

In a homogenous population of sedated postoperative patients, robust qEEG parameters strongly correlate with delirium and could serve as valuable biomarkers for early detection of delirium and assist in clinical decision-making.

Diversity of electroencephalographic patterns during propofol-induced burst suppression

Burst suppression is a brain state consisting of high-amplitude electrical activity alternating with periods of quieter suppression that can be brought about by disease or by certain anesthetics. Although burst suppression has been studied for decades, few studies have investigated the diverse manifestations of this state within and between human subjects. As part of a clinical trial examining the antidepressant effects of propofol, we gathered burst suppression electroencephalographic (EEG) data from 114 propofol infusions across 21 human subjects with treatment-resistant depression. This data was examined with the objective of describing and quantifying electrical signal diversity. We observed three types of EEG burst activity: canonical broadband bursts (as frequently described in the literature), spindles (narrow-band oscillations reminiscent of sleep spindles), and a new feature that we call low-frequency bursts (LFBs), which are brief deflections of mainly sub-3-Hz power. These three features were distinct in both the time and frequency domains and their occurrence differed significantly across subjects, with some subjects showing many LFBs or spindles and others showing very few. Spectral-power makeup of each feature was also significantly different across subjects. In a subset of nine participants with high-density EEG recordings, we noted that each feature had a unique spatial pattern of amplitude and polarity when measured across the scalp. Finally, we observed that the Bispectral Index Monitor, a commonly used clinical EEG monitor, does not account for the diversity of EEG features when processing the burst suppression state. Overall, this study describes and quantifies variation in the burst suppression EEG state across subjects and repeated infusions of propofol. These findings have implications for the understanding of brain activity under anesthesia and for individualized dosing of anesthetic drugs.

Always Assess the Raw Electroencephalogram: Why Automated Burst Suppression Detection May Not Detect All Episodes

BACKGROUND

Electroencephalogram (EEG)-based monitors of anesthesia are used to assess patients' level of sedation and hypnosis as well as to detect burst suppression during surgery. One of these monitors, the Entropy module, uses an algorithm to calculate the burst suppression ratio (BSR) that reflects the percentage of suppressed EEG. Automated burst suppression detection monitors may not reliably detect this EEG pattern. Hence, we evaluated the detection accuracy of BSR and investigated the EEG features leading to errors in the identification of burst suppression.

METHODS

With our study, we were able to compare the performance of the BSR to the visual burst suppression detection in the raw EEG and obtain insights on the architecture of the unrecognized burst suppression phases.

RESULTS

We showed that the BSR did not detect burst suppression in 13 of 90 (14%) patients. Furthermore, the time comparison between the visually identified burst suppression duration and elevated BSR values strongly depended on the BSR value being used as a cutoff. A possible factor for unrecognized burst suppression by the BSR may be a significantly higher suppression amplitude ( P = .002). Six of the 13 patients with undetected burst suppression by BSR showed intraoperative state entropy values >80, indicating a risk of awareness while being in burst suppression.

CONCLUSIONS

Our results complement previous results regarding the underestimation of burst suppression by other automated detection modules and highlight the importance of not relying solely on the processed index, but to assess the native EEG during anesthesia.

Delirium detection using GAMMA wave and machine learning: A pilot study

Delirium occurs in as many as 80% of critically ill older adults and is associated with increased long-term cognitive impairment, institutionalization, and mortality. Less than half of delirium cases are identified using currently available subjective assessment tools. Electroencephalogram (EEG) has been identified as a reliable objective measure but has not been feasible. This study was a prospective pilot proof-of-concept study, to examine the use of machine learning methods evaluating the use of gamma band to predict delirium from EEG data derived from a limited lead rapid response handheld device. Data from 13 critically ill participants aged 50 or older requiring mechanical ventilation for more than 12 h were enrolled. Across the three models, accuracy of predicting delirium was 70 or greater. Stepwise discriminant analysis provided the best overall method. While additional research is needed to determine the best cut points and efficacy, use of a handheld limited lead rapid response EEG device capable of monitoring all five cerebral lobes of the brain for predicting delirium hold promise.

A Common Human Brain-Derived Neurotrophic Factor Polymorphism Leads to Prolonged Depression of Excitatory Synaptic Transmission by Isoflurane in Hippocampal Cultures

Multiple presynaptic and postsynaptic targets have been identified for the reversible neurophysiological effects of general anesthetics on synaptic transmission and neuronal excitability. However, the synaptic mechanisms involved in persistent depression of synaptic transmission resulting in more prolonged neurological dysfunction following anesthesia are less clear. Here, we show that brain-derived neurotrophic factor (BDNF), a growth factor implicated in synaptic plasticity and dysfunction, enhances glutamate synaptic vesicle exocytosis, and that attenuation of vesicular BDNF release by isoflurane contributes to transient depression of excitatory synaptic transmission in mice. This reduction in synaptic vesicle exocytosis by isoflurane was acutely irreversible in neurons that release less endogenous BDNF due to a polymorphism (BDNF Val66Met; rs6265) compared to neurons from wild-type mice. These effects were prevented by exogenous application of BDNF. Our findings identify a role for a common human BDNF single nucleotide polymorphism in persistent changes of synaptic function following isoflurane exposure. These short-term persistent alterations in excitatory synaptic transmission indicate a role for human genetic variation in anesthetic effects on synaptic plasticity and neurocognitive function.

Spatial signatures of anesthesia-induced burst-suppression differ between primates and rodents

During deep anesthesia, the electroencephalographic (EEG) signal of the brain alternates between bursts of activity and periods of relative silence (suppressions). The origin of burst-suppression and its distribution across the brain remain matters of debate. In this work, we used functional magnetic resonance imaging (fMRI) to map the brain areas involved in anesthesia-induced burst-suppression across four mammalian species: humans, long-tailed macaques, common marmosets, and rats. At first, we determined the fMRI signatures of burst-suppression in human EEG-fMRI data. Applying this method to animal fMRI datasets, we found distinct burst-suppression signatures in all species. The burst-suppression maps revealed a marked inter-species difference: in rats, the entire neocortex engaged in burst-suppression, while in primates most sensory areas were excluded-predominantly the primary visual cortex. We anticipate that the identified species-specific fMRI signatures and whole-brain maps will guide future targeted studies investigating the cellular and molecular mechanisms of burst-suppression in unconscious states.

A Comparison of Brain-State Dynamics across Common Anesthetic Agents in Male Sprague-Dawley Rats

Anesthesia is a powerful tool in neuroscientific research, especially in sleep research where it has the experimental advantage of allowing surgical interventions that are ethically problematic in natural sleep. Yet, while it is well documented that different anesthetic agents produce a variety of brain states, and consequently have differential effects on a multitude of neurophysiological factors, these outcomes vary based on dosages, the animal species used, and the pharmacological mechanisms specific to each anesthetic agent. Thus, our aim was to conduct a controlled comparison of spontaneous electrophysiological dynamics at a surgical plane of anesthesia under six common research anesthetics using a ubiquitous animal model, the Sprague-Dawley rat. From this direct comparison, we also evaluated which anesthetic agents may serve as pharmacological proxies for the electrophysiological features and dynamics of unconscious states such as sleep and coma. We found that at a surgical plane, pentobarbital, isoflurane and propofol all produced a continuous pattern of burst-suppression activity, which is a neurophysiological state characteristically observed during coma. In contrast, ketamine-xylazine produced synchronized, slow-oscillatory activity, similar to that observed during slow-wave sleep. Notably, both urethane and chloral hydrate produced the spontaneous, cyclical alternations between forebrain activation (REM-like) and deactivation (non-REM-like) that are similar to those observed during natural sleep. Thus, choice of anesthesia, in conjunction with continuous brain state monitoring, are critical considerations in order to avoid brain-state confounds when conducting neurophysiological experiments.

Targeted Interventions to Increase Blood Pressure and Decrease Anaesthetic Concentrations Reduce Intraoperative Burst Suppression: A Randomised, Interventional Clinical Trial

Background

It has been suggested that intraoperative electroencephalographic (EEG) burst suppression (BSupp) may be associated with post-operative neurocognitive disorders in the elderly, and EEG-guided anaesthesia may help to reduce BSupp. Despite of this suggestion, a standard treatment does not exist, as we have yet to fully understand the phenomenon and its underlying pathomechanism. This study was designed to address two underlying phenomena—cerebral hypoperfusion and individual anaesthetic overdose.

Objectives

We aimed to demonstrate that targeted anaesthetic interventions—treating intraoperative hypotension and/or reducing the anaesthetic concentration—reduce BSupp.

Methods

We randomly assigned patients to receive EEG-based interventions during anaesthesia or EEG-blinded standard anaesthesia. If BSupp was detected, defined as burst suppression ratio (BSR) > 0, the primary intervention aimed to adjust the mean arterial blood pressure to patient baseline (MAP intervention) followed by reduction of anaesthetic concentration (MAC intervention).

Results

EEG-based intervention significantly reduced total cumulative BSR, BSR duration, and maximum BSR. MAP intervention caused a significant MAP increase at the end of a BSR > 0 episode compared to the control group. Coincidentally, the maximum BSR decreased significantly; in 55% of all MAP interventions, the BSR decreased to 0% without any further action. In the remaining events, additional MAC intervention was required.

Conclusion

Our results show that targeted interventions (MAC/MAP) reduce total cumulative amount, duration, and maximum BSR > 0 in the elderly undergoing general anaesthesia. Haemodynamic intervention already interrupted or reduced BSupp, strengthening the current reflections that hypotension-induced cerebral hypoperfusion may be seen as potential pathomechanism of intraoperative BSupp.

Clinical Trial Registration

NCT03775356 [ClinicalTrials.gov], DRKS00015839 [German Clinical Trials Register (Deutsches Register klinischer Studien, DRKS)].

Burst Suppression During General Anesthesia and Postoperative Outcomes: Mini Review

In the last decade, burst suppression has been increasingly studied by many to examine whether it is a mechanism leading to postoperative cognitive impairment. Despite a lack of consensus across trials, the current state of research suggests that electroencephalogram (EEG) burst suppression, duration and EEG emergence trajectory may predict postoperative delirium (POD). A mini literature review regarding evidence about burst suppression impact and susceptibilities was conducted, resulting in conflicting studies. Primarily, studies have used different algorithm values to replace visual burst suppression examination, although many studies have since emerged showing that algorithms underestimate burst suppression duration. As these methods may not be interchangeable with visual analysis of raw data, it is a potential factor for the current heterogeneity between data. Even though additional research trials incorporating the use of raw EEG data are necessary, the data currently show that monitoring with commercial intraoperative EEG machines that use EEG indices to estimate burst suppression may help physicians identify burst suppression and guide anesthetic titration during surgery. These modifications in anesthetics could lead to preventing unfavorable outcomes. Furthermore, some studies suggest that brain age, baseline impairment, and certain medications are risk factors for burst suppression and postoperative delirium. These patient characteristics, in conjunction with intraoperative EEG monitoring, could be used for individualized patient care. Future studies on the feasibility of raw EEG monitoring, new technologies for anesthetic monitoring and titration, and patient-associated risk factors are crucial to our continued understanding of burst suppression and postoperative delirium.

Evaluation of Anesthetic Specific EEG Dynamics during State Transitions between Loss and Return of Responsiveness

Purpose: electroencephalographic (EEG) information is used to monitor the level of cortical depression of a patient undergoing surgical intervention under general anesthesia. The dynamic state transitions into and out of anesthetic-induced loss and return of responsiveness (LOR, ROR) present a possibility to evaluate the dynamics of the EEG induced by different substances. We evaluated changes in the EEG power spectrum during anesthesia emergence for three different anesthetic regimens. We also assessed the possible impact of these changes on processed EEG parameters such as the permutation entropy (PeEn) and the cerebral state index (CSI). Methods: we analyzed the EEG from 45 patients, equally assigned to three groups. All patients were induced with propofol and the groups differed by the maintenance anesthetic regimen, i.e., sevoflurane, isoflurane, or propofol. We evaluated the EEG and parameter dynamics during LOR and ROR. For the emergence period, we focused on possible differences in the EEG dynamics in the different groups. Results: depending on the substance, the EEG emergence patterns showed significant differences that led to a substance-specific early activation of higher frequencies as indicated by the “wake” CSI values that occurred minutes before ROR in the inhalational anesthetic groups. Conclusion: our results highlight substance-specific differences in the emergence from anesthesia that can influence the EEG-based monitoring that probably have to be considered in order to improve neuromonitoring during general anesthesia.

Electroencephalographic Burst-Suppression, Perioperative Neuroprotection, Postoperative Cognitive Function, and Mortality: A Focused Narrative Review of the Literature

Burst-suppression is an electroencephalographic pattern that results from a diverse array of pathophysiological causes and/or metabolic neuronal suppression secondary to the administration of anesthetic medications. The purpose of this review is to provide an overview of the physiological mechanisms that underlie the burst-suppression pattern and to present in a comprehensive way the available evidence both supporting and in opposition to the clinical use of this electroencephalographic pattern as a therapeutic measure in various perioperative settings.

Revealing the Cortical Glutamatergic Neural Activity During Burst Suppression by Simultaneous wide Field Calcium Imaging and Electroencephalography in Mice

Burst suppression (BS) is an electroencephalogram (EEG) pattern in which signals alternates between high-amplitude slow waves (burst waves) and nearly flat low-amplitude waves (suppression waves). In this study, we used wide-field (8.32 mm × 8.32 mm) fluorescent calcium imaging to record the activity of glutamatergic neurons in the parietal and occipital cortex, in conjunction with EEG recordings under BS induced by different anesthetics (sevoflurane, isoflurane, and propofol), to investigate the spatiotemporal pattern of neural activity under BS. The calcium signal of all observed cortices was decreased during the phase of EEG suppression. However, during the phase of EEG burst, the calcium signal in areas of the medial cortex, such as the secondary motor and retrosplenial area, was excited, whereas the signal in areas of the lateral cortex, such as the hindlimb cortex, forelimb cortex, barrel field, and primary visual area, was still suppressed or only weakly excited. Correlation analysis showed a strong correlation between the EEG signal and the calcium signal in the medial cortex under BS (except for propofol induced signals). As the burst-suppression ratio (BSR) increased, the regions with strong correlation coefficients became smaller, but strong correlation coefficients were still noted in the medial cortex. Taken together, our results reveal the landscape of cortical activity underlying BS.

Etiology of Burst Suppression EEG Patterns

Burst-suppression electroencephalography (EEG) patterns of electrical activity, characterized by intermittent high-power broad-spectrum oscillations alternating with isoelectricity, have long been observed in the human brain during general anesthesia, hypothermia, coma and early infantile encephalopathy. Recently, commonalities between conditions associated with burst-suppression patterns have led to new insights into the origin of burst-suppression EEG patterns, their effects on the brain, and their use as a therapeutic tool for protection against deleterious neural states. These insights have been further supported by advances in mechanistic modeling of burst suppression. In this Perspective, we review the origins of burst-suppression patterns and use recent insights to weigh evidence in the controversy regarding the extent to which burst-suppression patterns observed during profound anesthetic-induced brain inactivation are associated with adverse clinical outcomes. Whether the clinical intent is to avoid or maintain the brain in a state producing burst-suppression patterns, monitoring and controlling neural activity presents a technical challenge. We discuss recent advances that enable monitoring and control of burst suppression.

Age-Related EEG Features of Bursting Activity During Anesthetic-Induced Burst Suppression

Electroencephalographic (EEG) Burst Suppression (BSUPP) is a discontinuous pattern characterized by episodes of low voltage disrupted by bursts of cortical synaptic activity. It can occur while delivering high-dose anesthesia. Current research suggests an association between BSUPP and the occurrence of postoperative delirium in the post-anesthesia care unit (PACU) and beyond. We investigated burst micro-architecture to further understand how age influences the neurophysiology of this pharmacologically-induced state. We analyzed a subset of EEG recordings (n = 102) taken from a larger data set previously published. We selected the initial burst that followed a visually identified “silent second,” i.e., at least 1 s of iso-electricity of the EEG during propofol induction. We derived the (normalized) power spectral density [(n)PSD], the alpha band power, the maximum amplitude, the maximum slope of the EEG as well as the permutation entropy (PeEn) for the first 1.5 s of the initial burst of each patient. In the old patients >65 years, we observed significantly lower (p < 0.001) EEG power in the 1–15 Hz range. In general, their EEG contained a significantly higher amount of faster oscillations (>15 Hz). Alpha band power (p < 0.001), EEG amplitude (p = 0.001), and maximum EEG slope (p = 0.045) all significantly decreased with age, whereas PeEn increased (p = 0.008). Hence, we can describe an age-related change in features during EEG burst suppression. Sub-group analysis revealed no change in results based on pre-medication. These EEG changes add knowledge to the impact of age on cortical synaptic activity. In addition to a reduction in EEG amplitude, age-associated burst features can complicate the identification of excessive anesthetic administration in patients under general anesthesia. Knowledge of these neurophysiologic changes may not only improve anesthesia care through improved detection of burst suppression but might also provide insight into changes in neuronal network organization in patients at risk for age-related neurocognitive problems.

The influence of induction speed on the frontal (processed) EEG

The intravenous injection of the anaesthetic propofol is clinical routine to induce loss of responsiveness (LOR). However, there are only a few studies investigating the influence of the injection rate on the frontal electroencephalogram (EEG) during LOR. Therefore, we focused on changes of the frontal EEG especially during this period. We included 18 patients which were randomly assigned to a slow or fast induction group and recorded the frontal EEG. Based on this data, we calculated the power spectral density, the band powers and band ratios. To analyse the behaviour of processed EEG parameters we calculated the beta ratio, the spectral entropy, and the spectral edge frequency. Due to the prolonged induction period in the slow injection group we were able to distinguish loss of responsiveness to verbal command (LOvR) from loss of responsiveness to painful stimulus (LOpR) whereas in the fast induction group we could not. At LOpR, we observed a higher relative alpha and beta power in the slow induction group while the relative power in the delta range was lower than in the fast induction group. When concentrating on the slow induction group the increase in relative alpha power pre-LOpR and even before LOvR indicated that frontal EEG patterns, which have been suggested as an indicator of unconsciousness, can develop before LOR. Further, LOvR was best reflected by an increase of the alpha to delta ratio, and LOpR was indicated by a decrease of the beta to alpha ratio. These findings highlight the different spectral properties of the EEG at various levels of responsiveness and underline the influence of the propofol injection rate on the frontal EEG during induction of general anesthesia.

Electroencephalographic features of discontinuous activity in anesthetized infants and children

Background

Discontinuous electroencephalographic activity in children is thought to reflect brain inactivation. Discontinuity has been observed in states of pathology, where it is predictive of adverse neurological outcome, as well as under general anesthesia. Though in preterm-infants discontinuity reflects normal brain development, less is known regarding its role in term children, particularly in the setting of general anesthesia. Here, we conduct a post-hoc exploratory analysis to investigate the spectral features of discontinuous activity in children under general anesthesia.

Methods

We previously recorded electroencephalography in children less than forty months of age under general anesthesia (n = 65). We characterized the relationship between age, anesthetic depth, and discontinuous activity, and used multitaper spectral methods to compare the power spectra of subjects with (n = 35) and without (n = 30) discontinuous activity. In the subjects with discontinuous activity, we examined the amplitude and power spectra associated with the discontinuities and analyzed how these variables varied with age.

Results

Cumulative time of discontinuity was associated with increased anesthetic depth and younger age. In particular, age-matched children with discontinuity received higher doses of propofol during induction as compared with children without discontinuity. In the tens of seconds preceding the onset of discontinuous activity, there was a decrease in high-frequency power in children four months and older that could be visually observed with spectrograms. During discontinuous activity, there were distinctive patterns of amplitude, spectral edge, and power in canonical frequency bands that varied with age. Notably, there was a decline in spectral edge in the seconds immediately following each discontinuity.

Conclusion

Discontinuous activity in children reflects a state of a younger or more deeply anesthetized brain, and characteristic features of discontinuous activity evolve with age and may reflect neurodevelopment.

Design and Implementation of a Machine Learning Based EEG Processor for Accurate Estimation of Depth of Anesthesia

Accurate monitoring of the depth of anesthesia (DoA) is essential for intraoperative and postoperative patient's health. Commercially available electroencephalograph (EEG)-based DoA monitors are recommended only for certain anesthetic drugs and specific age-group patients. This paper presents a machine learning classification processor for accurate DoA estimation irrespective of the patient's age and anesthetic drug. The classification is solely based on six features extracted from EEG signal, i.e., spectral edge frequency (SEF), beta ratio, and four bands of spectral energy (FBSE). A machine learning fine decision tree classifier is adopted to achieve a four-class DoA classification (deep, moderate, and light DoA versus awake state). The feature selection and the classification processor are optimized to achieve the highest classification accuracy for the state of moderate anesthesia required for the surgical operations. The proposed 256-point fast Fourier transform accelerator is implemented to realize SEF, beta ratio, and FBSE that enables minimal latency and high accuracy feature extraction. The proposed DoA processor is implemented using a 65 nm CMOS technology and experimentally verified using field programming gate array (FPGA) based on the EEG recordings of 75 patients undergoing elective surgery with different types of anesthetic agents. The processor achieves an average accuracy of 92.2% for all DoA states, with a latency of 1s The 0.09 mm² DoA processor consumes 140nJ/classification.