Changes in the electroencephalogram during anaesthesia and their physiological basis

Hagihira, Satoshi

doi:10.1093/bja/aev212

Cited by 202 publications

(82 citation statements)

References 14 publications

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“…3A,B). For older human infants, the correlation of the median EEG amplitude with the anesthetic concentration switched to a positive correlation, in agreement with adult human data ( 40 ). This relationship was even stronger using expected birth age, corrected for conceptional age (Fig.…”

Section: Resultssupporting

confidence: 87%

Identifying neurophysiological features associated with anesthetic state in newborn mice and humans

Chini

Gretenkord

et al. 2018

Preprint

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35Monitoring the hypnotic component of anesthesia during surgeries is critical to prevent 36 intraoperative awareness and reduce adverse side effects. For this purpose, 37 electroencephalographic methods complementing measures of autonomic functions and 38 behavioral responses are in use in clinical practice. However, in human neonates and 39 infants existing methods may be unreliable and the correlation between brain activity 40 and anesthetic depth is still poorly understood. Here, we characterize the effects of 41 different anesthetics on activity of several brain areas in neonatal mice and develop 42 machine learning approaches to identify electrophysiological features predicting inspired 43 or end-tidal anesthetic concentration as a proxy for anesthetic depth. We show that 44 similar features from electroencephalographic recordings can be applied to predict 45 anesthetic concentration in neonatal mice, and human neonates and infants. These 46 results might support a novel strategy to monitor anesthetic depth in human newborns. 47 49 Reliable monitoring of anesthesia depth is critical during surgery. It allows for loss 50 of consciousness, analgesia and immobility without incurring the risk of side effects and 51 complications due to anesthetic misdosing. Typically used measures to monitor 52 anesthesia depth are inspired and end-tidal anesthetic concentrations as well as 53 physiologic parameters, including respiratory rate and depth (in the absence of 54 neuromuscular blockade or controlled ventilation), heart rate, blood pressure, and 55 responses to noxious stimuli (1). These measures all respond to spinal and brainstem 56 reflexes and are not specific for arousal or cortical responses to noxious events. 57 Anesthesia-induced changes in brain activity can be measured with 58 electroencephalographic (EEG) recordings. Specific algorithms have been developed to 59 predict anesthesia depth in adults (2-4). The most commonly used of such methods, the 60 Bispectral Index, has been shown to significantly reduce intraoperative awareness, 61 amount of anesthetic used, recovery time and post-anesthesia care unit stay in a recent 62Cochrane meta-analysis (5), but see (6, 7). However, evidence of similar benefits in 63 infants and younger children is sparse, as recently shown (8-10). EEG in anesthetized 64 infants changes dramatically depending on postnatal age (8,(11)(12)(13)(14). 65 EEG recordings mainly monitor neocortical activity. Converging evidence from 66 animal and human studies has shown that most anesthetics slow 67 electroencephalographic oscillations (15)(16)(17). While power at high frequency oscillations 68 is reduced (>40 Hz), power at slower frequencies (<15 Hz) is enhanced (15). The 69 computations underling proprietary indexes such as the Bispectral index or Narcotrend 70 are thought to take advantage of these phenomena (18). However, in preterm and term 71 neonates for the first weeks of life, EEG during sleep-wake cycles is weakly correlated 72 Anesthesia and network dynamics during deve...

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Section: Resultssupporting

confidence: 87%

Identifying neurophysiological features associated with anesthetic state in newborn mice and humans

Chini

Gretenkord

et al. 2018

Preprint

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“…Various conditions have also been linked to changes in electrical brain activity, and can therefore be monitored to various extents using EEG. These include attention deficit hyperactivity disorder (ADHD) [10], disorders of consciousness [46,41], depth of anaesthesia [60], etc. EEG is also widely used in neuroscience and psychology research, as it is an excellent tool for studying the brain and its functioning.…”

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confidence: 99%

Deep learning-based electroencephalography analysis: a systematic review

et al. 2019

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Context. Electroencephalography (EEG) is a complex signal and can require several years of training, as well as advanced signal processing and feature extraction methodologies to be correctly interpreted. Recently, deep learning (DL) has shown great promise in helping make sense of EEG signals due to its capacity to learn good feature representations from raw data. Whether DL truly presents advantages as compared to more traditional EEG processing approaches, however, remains an open question.Objective. In this work, we review 156 papers that apply DL to EEG, published between January 2010 and July 2018, and spanning different application domains such as epilepsy, sleep, braincomputer interfacing, and cognitive and affective monitoring. We extract trends and highlight interesting approaches from this large body of literature in order to inform future research and formulate recommendations.Methods. Major databases spanning the fields of science and engineering were queried to identify relevant studies published in scientific journals, conferences, and electronic preprint repositories. Various data items were extracted for each study pertaining to 1) the data, 2) the preprocessing methodology, 3) the DL design choices, 4) the results, and 5) the reproducibility of the experiments. These items were then analyzed one by one to uncover trends. * The first two authors contributed equally to this work.Significance. To help the community progress and share work more effectively, we provide a list of recommendations for future studies. We also make our summary table of DL and EEG papers available and invite authors of published work to contribute to it directly.

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“…Burst suppression is characterized by isoelectric periods (suppression) punctuated by large-amplitude waves (bursts). 11 Burst suppression also occurs with coma and brain injury, but not during sleep. 12 Electroencephalogram suppression during surgery has been associated with postoperative delirium.…”

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confidence: 99%

Effect of Electroencephalography-Guided Anesthetic Administration on Postoperative Delirium Among Older Adults Undergoing Major Surgery

Wildes

Mickle

Abdallah

et al. 2019

JAMA

335

292

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IMPORTANCE Intraoperative electroencephalogram (EEG) waveform suppression, often suggesting excessive general anesthesia, has been associated with postoperative delirium. OBJECTIVE To assess whether EEG-guided anesthetic administration decreases the incidence of postoperative delirium. DESIGN, SETTING, AND PARTICIPANTS Randomized clinical trial of 1232 adults aged 60 years and older undergoing major surgery and receiving general anesthesia at Barnes-Jewish Hospital in St INTERVENTIONS Patients were randomized 1:1 (stratified by cardiac vs noncardiac surgery and positive vs negative recent fall history) to receive EEG-guided anesthetic administration (n = 614) or usual anesthetic care (n = 618). MAIN OUTCOMES AND MEASURES The primary outcome was incident delirium during postoperative days 1 through 5. Intraoperative measures included anesthetic concentration, EEG suppression, and hypotension. Adverse events included undesirable intraoperative movement, intraoperative awareness with recall, postoperative nausea and vomiting, medical complications, and death. RESULTSOf the 1232 randomized patients (median age, 69 years [range, 60 to 95]; 563 women [45.7%]), 1213 (98.5%) were assessed for the primary outcome. Delirium during postoperative days 1 to 5 occurred in 157 of 604 patients (26.0%) in the guided group and 140 of 609 patients (23.0%) in the usual care group (difference, 3.0% [95% CI, −2.0% to 8.0%]; P = .22). Median end-tidal volatile anesthetic concentration was significantly lower in the guided group than the usual care group (0.69 vs 0.80 minimum alveolar concentration; difference, −0.11 [95% CI, −0.13 to −0.10), and median cumulative time with EEG suppression was significantly less (7 vs 13 minutes; difference, −6.0 [95% CI, −9.9 to −2.1]). There was no significant difference between groups in the median cumulative time with mean arterial pressure below 60 mm Hg (7 vs 7 minutes; difference, 0.0 [95% CI, −1.7 to 1.7]). Undesirable movement occurred in 137 patients (22.3%) in the guided and 95 (15.4%) in the usual care group. No patients reported intraoperative awareness. Postoperative nausea and vomiting was reported in 48 patients (7.8%) in the guided and 55 patients (8.9%) in the usual care group. Serious adverse events were reported in 124 patients (20.2%) in the guided and 130 (21.0%) in the usual care group. Within 30 days of surgery, 4 patients (0.65%) in the guided group and 19 (3.07%) in the usual care group died.CONCLUSIONS AND RELEVANCE Among older adults undergoing major surgery, EEG-guided anesthetic administration, compared with usual care, did not decrease the incidence of postoperative delirium. This finding does not support the use of EEG-guided anesthetic administration for this indication. 40. Muhlhofer WG, Zak R, Kamal T, et al. Burst-suppression ratio underestimates absolute duration of electroencephalogram suppression compared with visual analysis of intraoperative electroencephalogram.

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Changes in the electroencephalogram during anaesthesia and their physiological basis

Cited by 202 publications

References 14 publications

Identifying neurophysiological features associated with anesthetic state in newborn mice and humans

Identifying neurophysiological features associated with anesthetic state in newborn mice and humans

Deep learning-based electroencephalography analysis: a systematic review

Effect of Electroencephalography-Guided Anesthetic Administration on Postoperative Delirium Among Older Adults Undergoing Major Surgery

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