Monitoring depth of anesthesia utilizing a combination of electroencephalographic and standard measures

An in-depth analysis of parameter settings and probability distributions of specific ordinal patterns in the Shannon permutation entropy during different states of consciousness in humans

As electrical activity in the brain has complex and dynamic properties, the complexity measure permutation entropy (PeEn) has proven itself to reliably distinguish consciousness states recorded by the EEG. However, it has been shown that the focus on specific ordinal patterns instead of all of them produced similar results. Moreover, parameter settings influence the resulting PeEn value. We evaluated the impact of the embedding dimension m and the length of the EEG segment on the resulting PeEn. Moreover, we analysed the probability distributions of monotonous and non-occurring ordinal patterns in different parameter settings. We based our analyses on simulated data as well as on EEG recordings from volunteers, obtained during stable anaesthesia levels at defined, individualised concentrations. The results of the analysis on the simulated data show a dependence of PeEn on different influencing factors such as window length and embedding dimension. With the EEG data, we demonstrated that the probability P of monotonous patterns performs like PeEn in lower embedding dimension (m = 3, AUC = 0.88, [0.7, 1] in both), whereas the probability P of non-occurring patterns outperforms both methods in higher embedding dimensions (m = 5, PeEn: AUC = 0.91, [0.77, 1]; P(non-occurring patterns): AUC = 1, [1, 1]). We showed that the accuracy of PeEn in distinguishing consciousness states changes with different parameter settings. Furthermore, we demonstrated that for the purpose of separating wake from anaesthesia EEG solely pieces of information used for PeEn calculation, i.e., the probability of monotonous patterns or the number of non-occurring patterns may be equally functional.

Information-Theoretic Approaches in EEG Correlates of Auditory Perceptual Awareness under Informational Masking

In informational masking paradigms, the successful segregation between the target and masker creates auditory perceptual awareness. The dynamics of the build-up of auditory perception is based on a set of interactions between bottom-up and top-down processes that generate neuronal modifications within the brain network activity. These neural changes are studied here using event-related potentials (ERPs), entropy, and integrated information, leading to several measures applied to electroencephalogram signals. The main findings show that the auditory perceptual awareness stimulated functional activation in the fronto-temporo-parietal brain network through (i) negative temporal and positive centro-parietal ERP components; (ii) an enhanced processing of multi-information in the temporal cortex; and (iii) an increase in informational content in the fronto-central cortex. These different results provide information-based experimental evidence about the functional activation of the fronto-temporo-parietal brain network during auditory perceptual awareness.

Meta-analysis of the effect of entropy-assisted general anesthesia on the quality of postoperative recovery

BACKGROUND

To evaluate the effect of the quality of postoperative anesthetic resuscitation in patients with entropy index monitoring assisted general anesthesia versus standard clinical practice.

METHODS

The randomized controlled trials on the application of entropy index monitoring in general anesthesia were searched in PubMed, Web of Science, Embase, The Cochrane Library, CNKI, Wanfang, VIP, and other databases by computer. The data were collected from inception to January 2022. Two researchers independently screened the retrieved literature according to the inclusion and exclusion criteria and used Cochrane's risk-of-bias assessment criteria to evaluate the quality of the literature. The evaluation indicators included respiratory recovery time, extubation time, consciousness recovery time, emergence agitation, postoperative nausea and vomiting (PONV), and intraoperative awareness. The RevMan 5.4.1 software was used for the meta-analysis of the data.

RESULTS

A total of 860 patients from 10 eligible randomized controlled trials were included in this study. The results showed that compared with the control group, the respiratory recovery time (MD = -3.37, 95% CI: -5.09 to -1.85, P < .0001), extubation time (MD = -4.57, 95% CI: -6.08 to -3.95, P < .00001), and consciousness recovery time (MD = -4.95, 95% CI: -7.21 to -2.70, P < .00001) in the entropy index group were significantly shortened. The incidence of emergence agitation in the entropy index group (RR = 0.23, 95% CI: 0.11-0.47, P < .0001) decreased significantly. The incidence of PONV (RR = 0.46, 95% CI: 0.27-0.79, P = .004) was significantly reduced. However, the incidence of intraoperative awareness (RR = 0.33, 95% CI: 0.04-3.16, P = .34) wasn't significantly different.

CONCLUSION

The application of the entropy index can improve the recovery quality of patients under general anesthesia, not only shortening the postoperative recovery time but also reducing the occurrence of agitation and PONV. It does not affect the incidence of intraoperative awareness.

Permutation entropy is not an age-independent parameter for EEG-based anesthesia monitoring

Background

An optimized anesthesia monitoring using electroencephalographic (EEG) information in the elderly could help to reduce the incidence of postoperative complications. Processed EEG information that is available to the anesthesiologist is affected by the age-induced changes of the raw EEG. While most of these methods indicate a "more awake" patient with age, the permutation entropy (PeEn) has been proposed as an age-independent measure. In this article, we show that PeEn is also influenced by age, independent of parameter settings.

Methods

We retrospectively analyzed the EEG of more than 300 patients, recorded during steady state anesthesia without stimulation, and calculated the PeEn for different embedding dimensions m that was applied to the EEG filtered to a wide variety of frequency ranges. We constructed linear models to evaluate the relationship between age and PeEn. To compare our results to published studies, we also performed a stepwise dichotomization and used non-parametric tests and effect sizes for pairwise comparisons.

Results

We found a significant influence of age on PeEn for all settings except for narrow band EEG activity. The analysis of the dichotomized data also revealed significant differences between old and young patients for the PeEn settings used in published studies.

Conclusion

Based on our findings, we could show the influence of age on PeEn. This result was independent of parameter, sample rate, and filter settings. Hence, age should be taken into consideration when using PeEn to monitor patient EEG.

Comparison of cerebral metabolic rate of oxygen, blood flow, and bispectral index under general anesthesia

SIGNIFICANCE

The optical measurement of cerebral oxygen metabolism was evaluated.

AIM

Compare optically derived cerebral signals to the electroencephalographic bispectral index (BIS) sensors to monitor propofol-induced anesthesia during surgery.

APPROACH

Relative cerebral metabolic rate of oxygen ( rCMRO 2 ) and blood flow (rCBF) were measured by time-resolved and diffuse correlation spectroscopies. Changes were tested against the relative BIS (rBIS) ones. The synchronism in the changes was also assessed by the R-Pearson correlation.

RESULTS

In 23 measurements, optically derived signals showed significant changes in agreement with rBIS: during propofol induction, rBIS decreased by 67% [interquartile ranges (IQR) 62% to 71%], rCMRO 2 by 33% (IQR 18% to 46%), and rCBF by 28% (IQR 10% to 37%). During recovery, a significant increase was observed for rBIS (48%, IQR 38% to 55%), rCMRO 2 (29%, IQR 17% to 39%), and rCBF (30%, IQR 10% to 44%). The significance and direction of the changes subject-by-subject were tested: the coupling between the rBIS, rCMRO 2 , and rCBF was witnessed in the majority of the cases (14/18 and 12/18 for rCBF and 19/21 and 13/18 for rCMRO 2 in the initial and final part, respectively). These changes were also correlated in time ( R > 0.69 to R = 1 , p - values < 0.05 ).

CONCLUSIONS

Optics can reliably monitor rCMRO 2 in such conditions.

How general anesthetics work: from the perspective of reorganized connections within the brain

General anesthesia is critical for various procedures and surgeries. Despite the widespread use of anesthetics, their precise mechanisms remain poorly understood. Anesthetics inevitably act on the brain, primarily through the modulation of target receptors. Even if the action is specific to an individual neuron, however, long-range effects can occur due to the tremendous interconnectedness of neuronal activity. The strength of this connectivity can be understood using mathematical models that allow for the study of neuronal connectivity dynamics. These models also allow researchers to develop hypotheses on the candidate mechanisms of action of different types of anesthesia. This review highlights the theoretical background associated with the study of the mechanisms of action of anesthetics. We propose a candidate framework that describes how anesthetics act on the brain and consciousness in general.

The Strength of Alpha Oscillations in the Electroencephalogram Differently Affects Algorithms Used for Anesthesia Monitoring

BACKGROUND

Intraoperative patient monitoring using the electroencephalogram (EEG) can help to adequately adjust the anesthetic level. Therefore, the processed EEG (pEEG) provides the anesthesiologist with the estimated anesthesia level. The commonly used approaches track the changes from a fast- and a low-amplitude EEG during wakefulness to a slow- and a high-amplitude EEG under general anesthesia. However, besides these changes, another EEG feature, a strong oscillatory activity in the alpha band (8-12 Hz), develops in the frontal EEG. Strong alpha-band activity during general anesthesia seems to reflect an appropriate anesthetic level for certain anesthetics, but the way the common pEEG approaches react to changes in the alpha-band activity is not well explained. Hence, we investigated the impact of an artificial alpha-band modulation on pEEG approaches used in anesthesia research.

METHODS

We performed our analyses based on 30 seconds of simulated sedation (n = 25) EEG, simulated anesthesia (n = 25) EEG, and EEG episodes from 20 patients extracted from a steady state that showed a clearly identifiable alpha peak in the density spectral array (DSA) and a state entropy (GE Healthcare) around 50, indicative of adequate anesthesia. From these traces, we isolated the alpha activity by band-pass filtering (8-12 Hz) and added this alpha activity to or subtracted it from the signals in a stepwise manner. For each of the original and modified signals, the following pEEG values were calculated: (1) spectral edge frequency (SEF95), (2) beta ratio, (3) spectral entropy (SpEntr), (4) approximate entropy (ApEn), and (5) permutation entropy (PeEn).

RESULTS

The pEEG approaches showed different reactions to the alpha-band modification that depended on the data set and the amplification step. The beta ratio and PeEn decreased with increasing alpha activity for all data sets, indicating a deepening of anesthesia. The other pEEG approaches behaved nonuniformly. SEF95, SpEntr, and ApEn decreased with increasing alpha for the simulated anesthesia data (arousal) but decreased for simulated sedation. For the patient EEG, ApEn indicated an arousal, and SEF95 and SpEntr showed a nonuniform change.

CONCLUSIONS

Changes in the alpha-band activity lead to different reactions for different pEEG approaches. Hence, the presence of strong oscillatory alpha activity that reflects an adequate level of anesthesia may be interpreted differently, by an either increasing (arousal) or decreasing (deepening) pEEG value. This could complicate anesthesia navigation and prevent the adjustment to an adequate, alpha-dominant anesthesia level, when titrating by the pEEG values.

A survey of current practices, attitudes and demands of anaesthesiologists regarding the depth of anaesthesia monitoring in China

Background

In this study, we aimed to analyse survey data to explore two different hypotheses; and for this purpose, we distributed an online survey to Chinese anaesthesiologists. The hypothetical questions in this survey include: (1) Chinese anaesthesiologists mainly use the depth of anaesthesia (DoA) monitors to prevent intraoperative awareness and (2) the accuracy of these monitors is the most crucial performance factor during the clinical daily practice of Chinese anaesthesiologists.

Methods

We collected and statistically analysed the response of a total of 12,750 anesthesiologists who were invited to participate in an anonymous online survey. The Chinese Society of Anaesthesiologists (CSA) trial group provided the email address of each anaesthesiologist, and the selection of respondents was random from the computerized system.

Results

The overall response rate was 32.0% (4037 respondents). Only 9.1% (95% confidence interval, 8.2-10.0%) of the respondents routinely used DoA monitors. Academic respondents (91.5, 90.3-92.7%) most frequently used DoA monitoring to prevent awareness, whereas nonacademic respondents (88.8, 87.4-90.2%) most frequently used DoA monitoring to guide the delivery of anaesthetic agents. In total, the number of respondents who did not use a DoA monitor and whose patients experienced awareness (61.7, 57.8-65.6%) was significantly greater than those who used one or several DoA monitors (51.5, 49.8-53.2%). Overall, the crucial performance factor during DoA monitoring was considered by 61.9% (60.4-63.4%) of the respondents to be accuracy. However, most respondents (95.7, 95.1-96.3%) demanded improvements in the accuracy of the monitors for DoA monitoring. In addition, broad application in patients of all ages (86.3, 85.2-87.4%), analgesia monitoring (80.4, 79.2-81.6%), and all types of anaesthetic agents (75.6, 74.3-76.9%) was reported. In total, 65.0% (63.6-66.5%) of the respondents believed that DoA monitors should be combined with EEG and vital sign monitoring, and 53.7% (52.1-55.2%) believed that advanced DoA monitors should include artificial intelligence.

Conclusions

Academic anaesthesiologists primarily use DoA monitoring to prevent awareness, whereas nonacademic anaesthesiologists use DoA monitoring to guide the delivery of anaesthetics. Anaesthesiologists demand high-accuracy DoA monitors incorporating EEG signals, multiple vital signs, and antinociceptive indicators. DoA monitors with artificial intelligence may represent a new direction for future research on DoA monitoring.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12871-021-01510-7.

Consciousness and complexity: a consilience of evidence

Over the last years, a surge of empirical studies converged on complexity-related measures as reliable markers of consciousness across many different conditions, such as sleep, anesthesia, hallucinatory states, coma, and related disorders. Most of these measures were independently proposed by researchers endorsing disparate frameworks and employing different methods and techniques. Since this body of evidence has not been systematically reviewed and coherently organized so far, this positive trend has remained somewhat below the radar. The aim of this paper is to make this consilience of evidence in the science of consciousness explicit. We start with a systematic assessment of the growing literature on complexity-related measures and identify their common denominator, tracing it back to core theoretical principles and predictions put forward more than 20 years ago. In doing this, we highlight a consistent trajectory spanning two decades of consciousness research and provide a provisional taxonomy of the present literature. Finally, we consider all of the above as a positive ground to approach new questions and devise future experiments that may help consolidate and further develop a promising field where empirical research on consciousness appears to have, so far, naturally converged.

Analyzing the Loss and the Recovery of Consciousness: Functional Connectivity Patterns and Changes in Heart Rate Variability During Propofol-Induced Anesthesia

The analysis of the central and the autonomic nervous systems (CNS, ANS) activities during general anesthesia (GA) provides fundamental information for the study of neural processes that support alterations of the consciousness level. In the present pilot study, we analyzed EEG signals and the heart rate (HR) variability (HRV) in a sample of 11 patients undergoing spinal surgery to investigate their CNS and ANS activities during GA obtained with propofol administration. Data were analyzed during different stages of GA: baseline, the first period of anesthetic induction, the period before the loss of consciousness, the first period after propofol discontinuation, and the period before the recovery of consciousness (ROC). In EEG spectral analysis, we found a decrease in posterior alpha and beta power in all cortical areas observed, except the occipital ones, and an increase in delta power, mainly during the induction phase. In EEG connectivity analysis, we found a significant increase of local efficiency index in alpha and delta bands between baseline and loss of consciousness as well as between baseline and ROC in delta band only and a significant reduction of the characteristic path length in alpha band between the baseline and ROC. Moreover, connectivity results showed that in the alpha band there was mainly a progressive increase in the number and in the strength of incoming connections in the frontal region, while in the beta band the parietal region showed mainly a significant increase in the number and in the strength of outcoming connections values. The HRV analysis showed that the induction of anesthesia with propofol was associated with a progressive decrease in complexity and a consequent increase in the regularity indexes and that the anesthetic procedure determined bradycardia which was accompanied by an increase in cardiac sympathetic modulation and a decrease in cardiac parasympathetic modulation during the induction. Overall, the results of this pilot study showed as propofol-induced anesthesia caused modifications on EEG signal, leading to a "rebalance" between long and short-range cortical connections, and had a direct effect on the cardiac system. Our data suggest interesting perspectives for the interactions between the central and autonomic nervous systems for the modulation of the consciousness level.

Simultaneous comparison of depth of sedation performance between SedLine and BIS during general anesthesia using custom passive interface hardware: study protocol for a prospective, non-blinded, non-randomized trial

Background

Intraoperative brain function monitoring with processed electroencephalogram (EEG) indices, such as the bispectral index (BIS) and patient state index (PSI), may improve characterization of the depth of sedation or anesthesia when compared to conventional physiologic monitors, such as heart rate and blood pressure. However, the clinical assessment of anesthetic depth may not always agree with available processed EEG indices. To concurrently compare the performance of BIS and SedLine monitors, we present a data collection system using shared individual generic sensors connected to a custom-built passive interface box.

Methods

This prospective, non-blinded, non-randomized study will enroll 100 adult American Society of Anesthesiologists (ASA) class I-III patients presenting for elective procedures requiring general anesthesia. BIS and SedLine electrodes will be placed preoperatively according to manufacturer recommendations and their respective indices tracked throughout anesthesia induction, maintenance and emergence. The concordance between processed EEG indices and clinical assessments of anesthesia depth will be analyzed with chi-square and kappa statistic.

Discussion

Prior studies comparing brain function monitoring devices have applied both sensors on the forehead of study subjects simultaneously. With limited space and common sensor locations between devices, it is not possible to place both commercial sensor arrays according to the manufacturer’s recommendations, thus compromising the validity of these comparisons. This trial utilizes a custom interface allowing signals from sensors to be shared between BIS and SedLine monitors to provide an accurate comparison. Our results will also characterize the degree of agreement between processed EEG indices and clinical assessments of anesthetic depth as determined by the anesthesiologists’ interpretations of acute changes in blood pressure and heart rate as well as the administration, or change to the continuous delivery, of medications at these timepoints. Patient factors (such as burst suppression state or low power EEG conditions from aging brain), surgical conditions (such as use of electrocautery), artifacts (such as electromyography), and anesthesia medications and doses (such as end-tidal concentration of volatile anesthetic or hypnotic infusion dose) that lead to lack of agreement will be explored as well.

Trial registration

Clinical Trials (ClinicalTrials.gov), NCT03865316. Registered on 4 February 2019 – retrospectively registered. Sponsor: Masimo Corporation.

Quantitative assessment of consciousness during anesthesia without EEG data

Assessing the depth of anesthesia (DoA) is a daily challenge for anesthesiologists. The best assessment of the depth of anesthesia is commonly thought to be the one made by the doctor in charge of the patient. This evaluation is based on the integration of several parameters including epidemiological, pharmacological and physiological data. By developing a protocol to record synchronously all these parameters we aim at having this evaluation made by an algorithm. Our hypothesis was that the standard parameters recorded during anesthesia (without EEG) could provide a good insight into the consciousness level of the patient. We developed a complete solution for high-resolution longitudinal follow-up of patients during anesthesia. A Hidden Markov Model (HMM) was trained on the database in order to predict and assess states based on four physiological variables that were adjusted to the consciousness level: Heart Rate (HR), Mean Blood Pressure (MeanBP) Respiratory Rate (RR), and AA Inspiratory Concentration (AAFi) all without using EEG recordings. Patients undergoing general anesthesia for hernial inguinal repair were included after informed consent. The algorithm was tested on 30 patients. The percentage of error to identify the actual state among Awake, LOC, Anesthesia, ROC and Emergence was 18%. This protocol constitutes the very first step on the way towards a multimodal approach of anesthesia. The fact that our first classifier already demonstrated a good predictability is very encouraging for the future. Indeed, this first model was merely a proof of concept to encourage research ways in the field of machine learning and anesthesia.

Predicting unconsciousness after propofol administration: qCON, BIS, and ALPHA band frequency power

During anesthesia induction with propofol the level of arousal progressively decreases until reaching loss of consciousness (LOC). In addition, there is a shift of alpha activity from parieto-occipital to frontal zones, defined as anteriorization. Whilst monitoring LOC and anteriorization would be useful to improve propofol dosage and patient safety, the current devices for anesthetic depth monitoring are unable to detect these events. The aim of this study was to observe LOC and anteriorization during anesthesia induction with propofol by applying electrodes placed in the frontal and parietal areas. Bispectral index (BIS) and quantium consciousness index (qCON) monitors were simultaneously employed. BIS™ and qCON sensors were placed in the frontal and parieto-occipital regions of 10 alopecic patients who underwent anesthesia with propofol, alfentanil, and remifentanil. The initial biophase target of propofol was 2.5 mcg/mL which was gradually increased until reaching LOC. Wilcoxon signed-rank test was used to study differences in alpha power and qCON/BIS indices along the study; and Pk value to evaluate predictive capability of anteriorization of BIS, qCON, and alpha waves. Parietal BIS and qCON values became significantly higher than frontal values 15 min after loss of eye reflex. Anteriorization was observed with both monitors. Pk values for BIS and qCON were strongly predictive of frontal alpha absolute power. During anesthesia induction with propofol it is possible to identify anteriorization with BIS and qCON in the frontal and parieto-occipital regions. Both indices showed different patterns which need to be further studied.

Spectral and Entropic Features Are Altered by Age in the Electroencephalogram in Patients under Sevoflurane Anesthesia

BACKGROUND

Preexisting factors such as age and cognitive performance can influence the electroencephalogram (EEG) during general anesthesia. Specifically, spectral EEG power is lower in elderly, compared to younger, subjects. Here, the authors investigate age-related changes in EEG architecture in patients undergoing general anesthesia through a detailed examination of spectral and entropic measures.

METHODS

The authors retrospectively studied 180 frontal EEG recordings from patients undergoing general anesthesia, induced with propofol/fentanyl and maintained by sevoflurane at the Waikato Hospital in Hamilton, New Zealand. The authors calculated power spectral density and normalized power spectral density, the entropic measures approximate and permutation entropy, as well as the beta ratio and spectral entropy as exemplary parameters used in current monitoring systems from segments of EEG obtained before the onset of surgery (i.e., with no noxious stimulation).

RESULTS

The oldest quartile of patients had significantly lower 1/f characteristics (P < 0.001; area under the receiver operating characteristics curve, 0.84 [0.76 0.92]), indicative of a more uniform distribution of spectral power. Analysis of the normalized power spectral density revealed no significant impact of age on relative alpha (P = 0.693; area under the receiver operating characteristics curve, 0.52 [0.41 0.63]) and a significant but weak effect on relative beta power (P = 0.041; area under the receiver operating characteristics curve, 0.62 [0.52 0.73]). Using entropic parameters, the authors found a significant age-related change toward a more irregular and unpredictable EEG (permutation entropy: P < 0.001, area under the receiver operating characteristics curve, 0.81 [0.71 0.90]; approximate entropy: P < 0.001; area under the receiver operating characteristics curve, 0.76 [0.66 0.85]). With approximate entropy, the authors could also detect an age-induced change in alpha-band activity (P = 0.002; area under the receiver operating characteristics curve, 0.69 [0.60 78]).

CONCLUSIONS

Like the sleep literature, spectral and entropic EEG features under general anesthesia change with age revealing a shift toward a faster, more irregular, oscillatory composition of the EEG in older patients. Age-related changes in neurophysiological activity may underlie these findings however the contribution of age-related changes in filtering properties or the signal to noise ratio must also be considered. Regardless, most current EEG technology used to guide anesthetic management focus on spectral features, and improvements to these devices might involve integration of entropic features of the raw EEG.

Apprenticeship Learning for a Predictive State Representation of Anesthesia

OBJECTIVE

In this paper, we present an original decision support algorithm to assist the anesthesiologists delivery of drugs to maintain the optimal Depth of Anesthesia (DoA).

METHODS

Derived from a Transform Predictive State Representation algorithm (TPSR), our model learned by observing anesthesiologists in practice. This framework, known as apprenticeship learning, is particularly useful in the medical field as it is not based on an exploratory process - a prohibitive behavior in healthcare. The model only relied on the four commonly monitored variables: Heart Rate (HR), the Mean Blood Pressure (MBP), the Respiratory Rate (RR) and the concentration of anesthetic drug (AAFi).

RESULTS

Thirty-one patients have been included. The performances of the model is analyzed with metrics derived from the Hamming distance and cross entropy. They demonstrated that low rank dynamical system had the best performances on both predictions and simulations. Then, a confrontation of our agent to a panel of six real anesthesiologists demonstrated that 95.7% of the actions were valid.

CONCLUSION

These results strongly support the hypothesis that TPSR based models convincingly embed the behavior of anesthesiologists including only four variables that are commonly assessed to predict the DoA.

SIGNIFICANCE

The proposed novel approach could be of great help for clinicians by improving the fine tuning of the DoA. Furthermore, the possibility to predict the evolutions of the variables would help preventing side effects such as low blood pressure. A tool that could autonomously help the anesthesiologist would thus improve safety-level in the surgical room.

Selection of the Best Electroencephalogram Channel to Predict the Depth of Anesthesia

Precise cerebral dynamics of action of the anesthetics are a challenge for neuroscientists. This explains why there is no gold standard for monitoring the Depth of Anesthesia (DoA) and why experimental studies may use several electroencephalogram (EEG) channels, ranging from 2 to 128 EEG-channels. Our study aimed at finding the scalp area providing valuable information about brain activity under general anesthesia (GA) to select the more optimal EEG channel to characterized the DoA. We included 30 patients undergoing elective, minor surgery under GA and used a 32-channel EEG to record their electrical brain activity. In addition, we recorded their physiological parameters and the BIS monitor. Each individual EEG channel data were processed to test their ability to differentiate awake from asleep states. Due to strict quality criteria adopted for the EEG data and the difficulties of the real-life setting of the study, only 8 patients recordings were taken into consideration in the final analysis. Using 2 classification algorithms, we identified the optimal channels to discriminate between asleep and awake states: the frontal and temporal F8 and T7 were retrieved as being the two bests channels to monitor DoA. Then, using only data from the F8 channel, we tried to minimize the number of features required to discriminate between the awake and asleep state. The best algorithm turned out to be the Gaussian Naïve Bayes (GNB) requiring only 5 features (Area Under the ROC Curve - AUC- of 0.93 ± 0.04). This finding may pave the way to improve the assessment of DoA by combining one EEG channel recordings with a multimodal physiological monitoring of the brain state under GA. Further work is needed to see if these results may be valid to asses the depth of sedation in ICU.

Anesthetists’ Heart Rate Variability as an Indicator of Performance During Induction of General Anesthesia and Simulated Critical Incidents

Abstract. In the environment of anesthesia, good performance describes the absence of threat for the patient as well as a quick reaction to challenging and possibly life-threatening circumstances. Elsewhere, performance and cognitive function have been linked to indicators of vagally-mediated heart rate variability (HRV). This exploratory study examines the correlation between anesthetists’ HRV and their performance during uneventful induction of general anesthesia and during a simulated critical incident. For this study electrocardiograms (ECG) were obtained from two different groups of anesthetists providing general anesthesia in uneventful real cases in the operation room (OR, n = 38) and during the management of a hypotension scenario in a high-fidelity human patient simulator environment (SIM, n = 23). Frequency, time domain, and nonlinear HRV metrics were calculated from 5-min ECG recordings. To separate high performing (HP) and low performing (LP) individuals, the time needed for induction (in the OR setting) and the length and depth of hypotension (in the SIM setting) were used as performance correlates. The Mann-Whitney- U-test was used to assess differences in HRV within the groups. In both settings (OR and SIM), linear and nonlinear HRV metrics did not differ significantly between the HP and LP group. Also, the anesthetists’ work experience and sex were not related to performance. While providing general anesthesia and during a simulated critical incident, high and low performing individuals do not differ with respect to HRV metrics, sex, and work experience. Further research including the HRV under resting conditions is necessary.

Novel drug-independent sedation level estimation based on machine learning of quantitative frontal electroencephalogram features in healthy volunteers

BACKGROUND

Sedation indicators based on a single quantitative EEG (QEEG) feature have been criticised for their limited performance. We hypothesised that integration of multiple QEEG features into a single sedation-level estimator using a machine learning algorithm could reliably predict levels of sedation, independent of the sedative drug used.

METHODS

In total, 102 subjects receiving propofol (N=36; 16 male/20 female), sevoflurane (N=36; 16 male/20 female), or dexmedetomidine (N=30; 15 male/15 female) were included in this study of healthy volunteers. Sedation level was assessed using the Modified Observer's Assessment of Alertness/Sedation (MOAA/S) score. We used 44 QEEG features estimated from the EEG data in a logistic regression algorithm, and an elastic-net regularisation method was used for feature selection. The area under the receiver operator characteristic curve (AUC) was used to assess the performance of the logistic regression model.

RESULTS

The performances obtained when the system was trained and tested as drug-dependent mode to distinguish between awake and sedated states (mean AUC [standard deviation]) were propofol=0.97 (0.03), sevoflurane=0.74 (0.25), and dexmedetomidine=0.77 (0.10). The drug-independent system resulted in mean AUC=0.83 (0.17) to discriminate between the awake and sedated states.

CONCLUSIONS

The incorporation of large numbers of QEEG features and machine learning algorithms is feasible for next-generation monitors of sedation level. Different QEEG features were selected for propofol, sevoflurane, and dexmedetomidine groups, but the sedation-level estimator maintained a high performance for predicting MOAA/S independent of the drug used.

CLINICAL TRIAL REGISTRATION

NCT02043938; NCT03143972.

Combined Monitoring-Brain Function Monitoring and Cerebral Oximetry

Peri-operative brain function monitoring is still seen by most clinicians as complex, difficult to interpret and is therefore adopted very slowly. Current available technology mainly focusses on either a processed parameter based on the electroencephalogram to titrate anesthetics and central acting agents or on cerebral oximetry, a wider term to obtain information on the cerebral oxygen balance. There is still a lack of technological offerings that allow to monitor both entities in one device. However, there is scientific evidence that it is possible to combine measurements in an algorithmic approach that allows to better manage brain function in the surgical setting. Such integrated solutions should be made available to clinicians as they are likely to optimize patient care dependent on a sound health technology assessment.

Anesthetics: from modes of action to unconsciousness and neurotoxicity

Modern anesthetic compounds and advanced monitoring tools have revolutionized the field of medicine, allowing for complex surgical procedures to occur safely and effectively. Faster induction times and quicker recovery periods of current anesthetic agents have also helped reduce health care costs significantly. Moreover, extensive research has allowed for a better understanding of anesthetic modes of action, thus facilitating the development of more effective and safer compounds. Notwithstanding the realization that anesthetics are a prerequisite to all surgical procedures, evidence is emerging to support the notion that exposure of the developing brain to certain anesthetics may impact future brain development and function. Whereas the data in support of this postulate from human studies is equivocal, the vast majority of animal research strongly suggests that anesthetics are indeed cytotoxic at multiple brain structure and function levels. In this review, we first highlight various modes of anesthetic action and then debate the evidence of harm from both basic science and clinical studies perspectives. We present evidence from animal and human studies vis-à-vis the possible detrimental effects of anesthetic agents on both the young developing and the elderly aging brain while discussing potential ways to mitigate these effects. We hope that this review will, on the one hand, invoke debate vis-à-vis the evidence of anesthetic harm in young children and the elderly, and on the other hand, incentivize the search for better and less toxic anesthetic compounds.

Analyzing Electroencephalography (EEG) Waves Provides a Reliable Tool to Assess the Depth of Sevoflurane Anesthesia in Pediatric Patients

BACKGROUND Studies have reported that BIS is unreliable in children because its algorithm provides misleading information about the actual depth of anesthesia. Raw EEG analysis provides direct neurophysiologic measurement of cerebral activity. The relationship between age and EEG has rarely been reported, thus the aim of the present study was to compare raw electroencephalography (EEG) among different age groups of surgical patients under general anesthesia with 1.0 MAC sevoflurane. MATERIAL AND METHODS We enrolled 135 patients aged 0-80 years old (ASA physical status I or II) undergoing surgery, who were divided into 6 groups: 1-12 months old (group 1), 1-3 years old (group 2), 3-6 years old (group 3), 6-18 years old (group 4), 18-65 years old (group 5), and 65-80 years old (group 6). Different raw EEG waves (alpha, delta, and theta) were compared for all subjects. RESULTS The BIS values in groups 1 to 6 were 52.2±12.7, 55.0±8.0, 44.5±7.3, 43.8±7.3, 44.2±6.2, and 49.1±6.2 respectively. Compared with groups 1 and 2 (52.2±12.7, 55.0±8.0), BIS values of groups 3, 4, and 5 (44.5±7.3, 43.8±7.3, 44.2±6.2, respectively) were lower (P<0.05). Theta frequency was observed in the 6 groups. The EEG frequencies in groups 1 to 6 were 6.0 (5.5-6.0), 6.0 (5.5-6.0), 6.0 (5.5-6.0), 6.0 (6.0-7.0), 6.3 (6.0-7.0), and 6.0 (5.1-6.0), respectively. Compared with group 6, EEG frequencies in groups 4 and 5 were higher (P<0.05). BIS value was significantly correlated with EEG frequency (R²=0.063, P<0.01). CONCLUSIONS Analyzing raw EEG waves provides more accurate judgement of depth of anesthesia, especially in pediatric cases in which monitors often provide misleading values.

Passive limb movement test facilitates subthalamic deep brain stimulation under general anesthesia without influencing awareness

Objectives:

We have shown that neuronal activity in the subthalamic nucleus (STN) in patients with Parkinson's disease can be accurately recorded during deep brain stimulation (DBS) with general anesthesia (GA). However, a vigorous passive range of motion (PROM) test might exert awakening effects on patients who are lightly anesthetized. We will explore the effects of PROM on the heart rate (HR) and mean arterial pressure (MAP) during microelectrode recording (MER) and confirm whether it facilitates identifying the sensory motor portion of the STN under GA.

Materials and Methods:

3T magnetic resonance image targeting of the STN was done to guide MER during frame-based stereotactic procedures for DBS. Regular induction and endotracheal intubation for GA were performed and then maintained with a volatile anesthetic agent and muscle relaxant only. The depth of anesthesia was monitored by the bispectral index (BIS).

Results:

A total of ten patients were enrolled in this study. Their mean age was 48.5 ± 10.8 years old with a disease duration 8.6 ± 2.4 years at the time of surgery. During MER, PROM significantly decreased recording tract numbers and still reached the STN at a recorded length at 5.5 ± 0.8 mm. Compared with baseline, PROM increased HR by a mean 0.5 beats/min and MAP by a mean 1.4 mmHg (P = 0.1178 and 0.0525). The change in BIS was −0.7 (P = 0.4941), and the mean alveolar concentration of the anesthetic agent changed little throughout surgery.

Conclusions:

PROM was effective in triggering and magnifying neuronal firing signal without influencing patient awareness during MER for STN-DBS under GA.

Linear and non-linear heart rate metrics for the assessment of anaesthetists' workload during general anaesthesia

BACKGROUND

Excessive workload may impact the anaesthetists' ability to adequately process information during clinical practice in the operation room and may result in inaccurate situational awareness and performance. This exploratory study investigated heart rate (HR), linear and non-linear heart rate variability (HRV) metrics and subjective ratings scales for the assessment of workload associated with the anaesthesia stages induction, maintenance and emergence.

METHODS

HR and HRV metrics were calculated based on five min segments from each of the three anaesthesia stages. The area under the receiver operating characteristics curve (AUC) of the investigated metrics was calculated to assess their ability to discriminate between the stages of anaesthesia. Additionally, a multiparametric approach based on logistic regression models was performed to further evaluate whether linear or non-linear heart rate metrics are suitable for the assessment of workload.

RESULTS

Mean HR and several linear and non-linear HRV metrics including subjective workload ratings differed significantly between stages of anaesthesia. Permutation Entropy (PeEn, AUC=0.828) and mean HR (AUC=0.826) discriminated best between the anaesthesia stages induction and maintenance. In the multiparametric approach using logistic regression models, the model based on non-linear heart rate metrics provided a higher AUC compared with the models based on linear metrics.

CONCLUSIONS

In this exploratory study based on short ECG segment analysis, PeEn and HR seem to be promising to separate workload levels between different stages of anaesthesia. The multiparametric analysis of the regression models favours non-linear heart rate metrics over linear metrics.

Use, applicability and reliability of depth of hypnosis monitors in children - a survey among members of the European Society for Paediatric Anaesthesiology

Background

To assess the thoughts of practicing anaesthesiologists about the use of depth of hypnosis monitors in children.

Methods

Members of the European Society for Paediatric Anaesthesiology were invited to participate in an online survey about their thoughts regarding the use, applicability and reliability of hypnosis monitoring in children.

Results

The survey achieved a response rate of 30% (N = 168). A total of 138 completed surveys were included for further analysis. Sixty-eight respondents used hypnosis monitoring in children (Users) and 70 did not (Non-users). Sixty-five percent of the Users reported prevention of intra-operative awareness as their main reason to apply hypnosis monitoring. Among the Non-users, the most frequently given reason (43%) not to use hypnosis monitoring in children was the perceived lack or reliability of the devices in children. Hypnosis monitoring is used with a higher frequency during propofol anaesthesia than during inhalation anaesthesia. Hypnosis monitoring is furthermore used more frequently in children > 4 years than in younger children. An ideal hypnosis monitor should be reliable for all age groups and any (combination of) anaesthetic drug. We found no agreement in the interpretation of monitor index values and subsequent anaesthetic interventions following from it.

Conclusions

Prevention of intraoperative awareness appears to be the most important reason to use hypnosis monitoring in children. The perceived lack of reliability of hypnosis monitoring in children is the most important reasons not to use it. No consensus currently exists on how to adjust anaesthesia according to hypnosis monitor index values in children.

Electronic supplementary material

The online version of this article (10.1186/s12871-018-0503-y) contains supplementary material, which is available to authorized users.

Non-instrumental clinical monitoring does not guarantee an adequate course of general anesthesia. A prospective clinical study

BACKGROUND

Clinical monitoring is the most common method of adjusting the appropriate level of general anesthesia. However, episodes of intraoperative awareness (AWR) are still reported, suggesting that clinical observations may not be sufficient in some cases. The objective of this study was to compare the efficacy of clinical and instrumental neuromonitoring with auditory evoked potentials (AEP) in an intraoperative analysis of the proper level of general anesthesia.

METHODS

Patients scheduled for elective surgery were randomly divided into two groups. Subjects in the first group underwent intravenous, in the second group volatile anesthesia. The adequacy of anesthesia was analyzed using clinical parameters. All the participants were instrumentally monitored with the autoregressive AEP index (AAI). After the anesthesia, patients filled out a questionnaire on possible AWR.

RESULTS

Data of 208 patients (87 in the first, and 121 in the second group) were analyzed. Before surgery there were no changes in AAI values between groups (80 vs. 78, P=0.5192). The mean values of clinical parameters changed, but five minutes after the nociceptive stimuli. The mean values of AAI at analyzed time points were specific for general anesthesia. In patients under intravenous anesthesia, we found more episodes of too low (46/608 vs.15/847, P<0.000) anesthesia. One case of AWR was found in the TIVA group.

CONCLUSIONS

AAI index is good indicator of patients' level of consciousness during general anesthesia. Standard clinical monitoring provides appropriate level of the procedure. However, it is insufficient during TIVA and does not prevent episodes of AWR.

The validity of linear and non-linear heart rate metrics as workload indicators of emergency physicians

Background

It has been shown that linear and non-linear heart rate variability (HRV) metrics are suitable to assess workload of anesthetists administering anesthesia. In pre-hospital emergency care, these parameters have not yet been evaluated. We hypothesized that heart rate (HR) and HRV metrics discriminate between differing workload levels of an emergency physician.

Methods

Electrocardiograms were obtained from 13 emergency physicians. Mean HR, ten linear and seven non-linear HRV metrics were analyzed. For each sortie, four different levels of workload were defined. Mixed-effects models and the area under the receiver operating characteristics curve (AUC) were used to test and quantify the HR and HRV metrics’ ability to discriminate between levels of workload. This was conducted for mean HR and each HRV metric as well as for groups of metrics (time domain vs. frequency domain vs. non-linear metrics).

Results

The non-linear HRV metric Permutation entropy (PeEn) discriminated best between the time before the alarm and primary patient care (AUC = 0.998, 1st rank of 18 HRV metrics). In contrast, AUC of the mean HR was low (0.558, 17th rank). In the multivariable approach, the non-linear HRV metrics provided a higher AUC (0.998) compared to the frequency domain (0.677) and to the time domain metrics (0.680).

Conclusion

Non-linear heart rate metrics and, specifically, PeEn provided good validity for the assessment of different levels of a physician’s workload in the setting of pre-hospital emergency care. In contradiction to earlier findings, the physicians’ mean HR was not a valid marker of workload.

Neural Correlates of Sevoflurane-induced Unconsciousness Identified by Simultaneous Functional Magnetic Resonance Imaging and Electroencephalography

BACKGROUND

The neural correlates of anesthetic-induced unconsciousness have yet to be fully elucidated. Sedative and anesthetic states induced by propofol have been studied extensively, consistently revealing a decrease of frontoparietal and thalamocortical connectivity. There is, however, less understanding of the effects of halogenated ethers on functional brain networks.

METHODS

The authors recorded simultaneous resting-state functional magnetic resonance imaging and electroencephalography in 16 artificially ventilated volunteers during sevoflurane anesthesia at burst suppression and 3 and 2 vol% steady-state concentrations for 700 s each to assess functional connectivity changes compared to wakefulness. Electroencephalographic data were analyzed using symbolic transfer entropy (surrogate of information transfer) and permutation entropy (surrogate of cortical information processing). Functional magnetic resonance imaging data were analyzed by an independent component analysis and a region-of-interest-based analysis.

RESULTS

Electroencephalographic analysis showed a significant reduction of anterior-to-posterior symbolic transfer entropy and global permutation entropy. At 2 vol% sevoflurane concentrations, frontal and thalamic networks identified by independent component analysis showed significantly reduced within-network connectivity. Primary sensory networks did not show a significant change. At burst suppression, all cortical networks showed significantly reduced functional connectivity. Region-of-interest-based thalamic connectivity at 2 vol% was significantly reduced to frontoparietal and posterior cingulate cortices but not to sensory areas.

CONCLUSIONS

Sevoflurane decreased frontal and thalamocortical connectivity. The changes in blood oxygenation level dependent connectivity were consistent with reduced anterior-to-posterior directed connectivity and reduced cortical information processing. These data advance the understanding of sevoflurane-induced unconsciousness and contribute to a neural basis of electroencephalographic measures that hold promise for intraoperative anesthesia monitoring.

Sleep Features on Continuous Electroencephalography Predict Rehabilitation Outcomes After Severe Traumatic Brain Injury

OBJECTIVE

Sleep characteristics detected by electroencephalography (EEG) may be predictive of neurological recovery and rehabilitation outcomes after traumatic brain injury (TBI). We sought to determine whether sleep features were associated with greater access to rehabilitation therapies and better functional outcomes after severe TBI.

METHODS

We retrospectively reviewed records of patients admitted with severe TBI who underwent 24 or more hours of continuous EEG (cEEG) monitoring within 14 days of injury for sleep elements and ictal activity. Patient outcomes included discharge disposition and modified Rankin Scale (mRS).

RESULTS

A total of 64 patients underwent cEEG monitoring for a mean of 50.6 hours. Status epilepticus or electrographic seizures detected by cEEG were associated with poor outcomes (death or discharge to skilled nursing facility). Sleep characteristics were present in 19 (30%) and associated with better outcome (89% discharged to home/acute rehabilitation; P = .0002). Lack of sleep elements on cEEG correlated with a poor outcome or mRS > 4 at hospital discharge (P = .012). Of those patients who were transferred to skilled nursing/acute rehabilitation, sleep architecture on cEEG associated with a shorter inpatient hospital stay (20 days vs 27 days) and earlier participation in therapy (9.8 days vs 13.2 days postinjury). Multivariable analyses indicated that sleep features on cEEG predicted functional outcomes independent of admission Glasgow Coma Scale and ictal-interictal activity.

CONCLUSION

The presence of sleep features in the acute period after TBI indicates earlier participation in rehabilitative therapies and a better functional recovery. By contrast, status epilepticus, other ictal activity, or absent sleep architecture may portend a worse prognosis. Whether sleep elements detected by EEG predict long-term prognosis remains to be determined.

Consciousness fluctuation during general anesthesia: a theoretical approach to anesthesia awareness and memory modulation

With anesthesia awareness as a model of study we debate the both fascinating and dangerous phenomenon called consciousness fluctuation that takes place during surgical anesthesia. In accordance with current scientific knowledge this paradox is the consequence of our limits in both precise knowledge of anesthesia mechanisms and our inability to accurately assess the level of anesthesia with brain monitoring. We also focus on the relationships between memory and anesthesia, as well as the possibility of interfering with memory during general anesthesia.

Tracking Electroencephalographic Changes Using Distributions of Linear Models: Application to Propofol-Based Depth of Anesthesia Monitoring

OBJECTIVE

Tracking brain states with electrophysiological measurements often relies on short-term averages of extracted features and this may not adequately capture the variability of brain dynamics. The objective is to assess the hypotheses that this can be overcome by tracking distributions of linear models using anesthesia data, and that anesthetic brain state tracking performance of linear models is comparable to that of a high performing depth of anesthesia monitoring feature.

METHODS

Individuals' brain states are classified by comparing the distribution of linear (auto-regressive moving average-ARMA) model parameters estimated from electroencephalographic (EEG) data obtained with a sliding window to distributions of linear model parameters for each brain state. The method is applied to frontal EEG data from 15 subjects undergoing propofol anesthesia and classified by the observers assessment of alertness/sedation (OAA/S) scale. Classification of the OAA/S score was performed using distributions of either ARMA parameters or the benchmark feature, Higuchi fractal dimension.

RESULTS

The highest average testing sensitivity of 59% (chance sensitivity: 17%) was found for ARMA (2,1) models and Higuchi fractal dimension achieved 52%, however, no statistical difference was observed. For the same ARMA case, there was no statistical difference if medians are used instead of distributions (sensitivity: 56%).

CONCLUSION

The model-based distribution approach is not necessarily more effective than a median/short-term average approach, however, it performs well compared with a distribution approach based on a high performing anesthesia monitoring measure.

SIGNIFICANCE

These techniques hold potential for anesthesia monitoring and may be generally applicable for tracking brain states.

Intraoperative Neurophysiological Monitoring for Endoscopic Endonasal Approaches to the Skull Base: A Technical Guide

Intraoperative neurophysiological monitoring during endoscopic, endonasal approaches to the skull base is both feasible and safe. Numerous reports have recently emerged from the literature evaluating the efficacy of different neuromonitoring tests during endonasal procedures, making them relatively well-studied. The authors report on a comprehensive, multimodality approach to monitoring the functional integrity of at risk nervous system structures, including the cerebral cortex, brainstem, cranial nerves, corticospinal tract, corticobulbar tract, and the thalamocortical somatosensory system during endonasal surgery of the skull base. The modalities employed include electroencephalography, somatosensory evoked potentials, free-running and electrically triggered electromyography, transcranial electric motor evoked potentials, and auditory evoked potentials. Methodological considerations as well as benefits and limitations are discussed. The authors argue that, while individual modalities have their limitations, multimodality neuromonitoring provides a real-time, comprehensive assessment of nervous system function and allows for safer, more aggressive management of skull base tumors via the endonasal route.

Mechanisms underlying brain monitoring during anesthesia: limitations, possible improvements, and perspectives

Currently, anesthesiologists use clinical parameters to directly measure the depth of anesthesia (DoA). This clinical standard of monitoring is often combined with brain monitoring for better assessment of the hypnotic component of anesthesia. Brain monitoring devices provide indices allowing for an immediate assessment of the impact of anesthetics on consciousness. However, questions remain regarding the mechanisms underpinning these indices of hypnosis. By briefly describing current knowledge of the brain's electrical activity during general anesthesia, as well as the operating principles of DoA monitors, the aim of this work is to simplify our understanding of the mathematical processes that allow for translation of complex patterns of brain electrical activity into dimensionless indices. This is a challenging task because mathematical concepts appear remote from clinical practice. Moreover, most DoA algorithms are proprietary algorithms and the difficulty of exploring the inner workings of mathematical models represents an obstacle to accurate simplification. The limitations of current DoA monitors — and the possibility for improvement — as well as perspectives on brain monitoring derived from recent research on corticocortical connectivity and communication are also discussed.

Toward Solving the Sedation-Assessment Conundrum: Neurofunction Monitoring

The sedation-assessment conundrum is the struggle to balance the need for sedation against the need to awaken the patient and perform a neurologic examination. This article discusses the nuances of the sedation-assessment conundrum as well as approaches to resolve this and reduce the negative impact of abruptly stopping sedative infusions. Both oversedation and undersedation affect critically ill patients. This article discusses methods of assessing sedation and interpreting individualized patient responses to sedation. The use of neurofunction monitors and periods of sedation interruption are discussed within the context of addressing the sedation-assessment conundrum.

The discriminatory value of cardiorespiratory interactions in distinguishing awake from anaesthetised states: a randomised observational study

Depth of anaesthesia monitors usually analyse cerebral function with or without other physiological signals; non‐invasive monitoring of the measured cardiorespiratory signals alone would offer a simple, practical alternative. We aimed to investigate whether such signals, analysed with novel, non‐linear dynamic methods, would distinguish between the awake and anaesthetised states. We recorded ECG, respiration, skin temperature, pulse and skin conductivity before and during general anaesthesia in 27 subjects in good cardiovascular health, randomly allocated to receive propofol or sevoflurane. Mean values, variability and dynamic interactions were determined. Respiratory rate (p = 0.0002), skin conductivity (p = 0.03) and skin temperature (p = 0.00006) changed with sevoflurane, and skin temperature (p = 0.0005) with propofol. Pulse transit time increased by 17% with sevoflurane (p = 0.02) and 11% with propofol (p = 0.007). Sevoflurane reduced the wavelet energy of heart (p = 0.0004) and respiratory (p = 0.02) rate variability at all frequencies, whereas propofol decreased only the heart rate variability below 0.021 Hz (p < 0.05). The phase coherence was reduced by both agents at frequencies below 0.145 Hz (p < 0.05), whereas the cardiorespiratory synchronisation time was increased (p < 0.05). A classification analysis based on an optimal set of discriminatory parameters distinguished with 95% success between the awake and anaesthetised states. We suggest that these results can contribute to the design of new monitors of anaesthetic depth based on cardiovascular signals alone.

A quest to increase safety of anesthetics by advancements in anesthesia monitoring: scientometric analysis

The aim of this study was to assess progress in the field of anesthesia monitoring over the past 40 years using scientometric analysis. The following scientometric indexes were used: popularity indexes (general and specific), representing the proportion of articles on either a topic relative to all articles in the field of anesthetics (general popularity index, GPI) or the subfield of anesthesia monitoring (specific popularity index, SPI); index of change (IC), representing the degree of growth in publications on a topic from one period to the next; and index of expectations (IE), representing the ratio of the number of articles on a topic in the top 20 journals relative to the number of articles in all (>5,000) biomedical journals covered by PubMed. Publications on 33 anesthesia-monitoring topics were assessed. Our analysis showed that over the past 40 years, the rate of rise in the number of articles on anesthesia monitoring was exponential, with an increase of more than eleven-fold, from 296 articles over the 5-year period 1974–1978 to 3,394 articles for 2009–2013. This rise profoundly exceeded the rate of rise of the number of articles on general anesthetics. The difference was especially evident with the comparison of the related GPIs: stable growth of the GPI for anesthesia monitoring vs constant decline in the GPI for general anesthetics. By the 2009–2013 period, among specific monitoring topics introduced after 1980, the SPI index had a meaningful magnitude (≥1.5) in 9 of 24 topics: Bispectral Index (7.8), Transesophageal Echocardiography (4.2), Electromyography (2.8), Pulse Oximetry (2.4), Entropy (2.3), Train-of-four (2.3), Capnography (1.9), Pulse Contour (1.9), and Electrical Nerve Stimulation for neuromuscular monitoring (1.6). Only one of these topics (Pulse Contour) demonstrated (in 2009–2013) high values for both IC and IE indexes (76 and 16.9, respectively), indicating significant recent progress. We suggest that rapid growth in the field of anesthetic monitoring was one of the most important developments to compensate for the intrinsically low margins of safety of anesthetic agents.