Quantifying cortical activity during general anesthesia using wavelet analysis

Depth of Anesthesia and Nociception Monitoring: Current State and Vision For 2050

Anesthesia objectives have evolved into combining hypnosis, amnesia, analgesia, paralysis, and suppression of the sympathetic autonomic nervous system. Technological improvements have led to new monitoring strategies, aimed at translating a qualitative physiological state into quantitative metrics, but the optimal strategies for depth of anesthesia (DoA) and analgesia monitoring continue to stimulate debate. Historically, DoA monitoring used patient's movement as a surrogate of awareness. Pharmacokinetic models and metrics, including minimum alveolar concentration for inhaled anesthetics and target-controlled infusion models for intravenous anesthesia, provided further insights to clinicians, but electroencephalography and its derivatives (processed EEG; pEEG) offer the potential for personalization of anesthesia care. Current studies appear to affirm that pEEG monitoring decreases the quantity of anesthetics administered, diminishes postanesthesia care unit duration, and may reduce the occurrence of postoperative delirium (notwithstanding the difficulties of defining this condition). Major trials are underway to further elucidate the impact on postoperative cognitive dysfunction. In this manuscript, we discuss the Bispectral (BIS) index, Narcotrend monitor, Patient State Index, entropy-based monitoring, and Neurosense monitor, as well as middle latency evoked auditory potential, before exploring how these technologies could evolve in the upcoming years. In contrast to developments in pEEG monitors, nociception monitors remain by comparison underdeveloped and underutilized. Just as with anesthetic agents, excessive analgesia can lead to harmful side effects, whereas inadequate analgesia is associated with increased stress response, poorer hemodynamic conditions and coagulation, metabolic, and immune system dysregulation. Broadly, 3 distinct monitoring strategies have emerged: motor reflex, central nervous system, and autonomic nervous system monitoring. Generally, nociceptive monitors outperform basic clinical vital sign monitoring in reducing perioperative opioid use. This manuscript describes pupillometry, surgical pleth index, analgesia nociception index, and nociception level index, and suggest how future developments could impact their use. The final section of this review explores the profound implications of future monitoring technologies on anesthesiology practice and envisages 3 transformative scenarios: helping in creation of an optimal analgesic drug, the advent of bidirectional neuron-microelectronic interfaces, and the synergistic combination of hypnosis and virtual reality.

Robotic Anesthesia: A Vision for 2050

The last 2 decades have brought important developments in anesthetic technology, including robotic anesthesia. Anesthesiologists titrate the administration of pharmacological agents to the patients' physiology and the needs of surgery, using a variety of sophisticated equipment (we use the term "pilots of the human biosphere"). In anesthesia, increased safety seems coupled with increased technology and innovation. This article gives an overview of the technological developments over the past decades, both in terms of pharmacological and mechanical robots, which have laid the groundwork for robotic anesthesia: target-controlled drug infusion systems, closed-loop administration of anesthesia and sedation, mechanical robots for intubation, and the latest development in the world of communication with the arrival of artificial intelligence (AI)-derived chatbots are presented.

Consciousness and General Anesthesia: Challenges for Measuring the Depth of Anesthesia

The optimal consciousness level required for general anesthesia with surgery is unclear, but in existing practice, anesthetic oblivion, may be incomplete. This article discusses the concept of consciousness, how it is altered by anesthetics, the challenges for assessing consciousness, currently used technologies for assessing anesthesia levels, and future research directions. Wakefulness is marked by a subjective experience of existence (consciousness), perception of input from the body or the environment (connectedness), the ability for volitional responsiveness, and a sense of continuity in time. Anesthetic drugs may selectively impair some of these components without complete extinction of the subjective experience of existence. In agreement with Sanders et al. (2012), the authors propose that a state of disconnected consciousness is the optimal level of anesthesia, as it likely avoids both awareness and the possible dangers of oversedation. However, at present, there are no reliably tested indices that can discriminate between connected consciousness, disconnected consciousness, and complete unconsciousness.

Continuous action deep reinforcement learning for propofol dosing during general anesthesia

PURPOSE

Anesthesiologists simultaneously manage several aspects of patient care during general anesthesia. Automating administration of hypnotic agents could enable more precise control of a patient's level of unconsciousness and enable anesthesiologists to focus on the most critical aspects of patient care. Reinforcement learning (RL) algorithms can be used to fit a mapping from patient state to a medication regimen. These algorithms can learn complex control policies that, when paired with modern techniques for promoting model interpretability, offer a promising approach for developing a clinically viable system for automated anesthestic drug delivery.

METHODS

We expand on our prior work applying deep RL to automated anesthetic dosing by now using a continuous-action model based on the actor-critic RL paradigm. The proposed RL agent is composed of a policy network that maps observed anesthetic states to a continuous probability density over propofol-infusion rates and a value network that estimates the favorability of observed states. We train and test three versions of the RL agent using varied reward functions. The agent is trained using simulated pharmacokinetic/pharmacodynamic models with randomized parameters to ensure robustness to patient variability. The model is tested on simulations and retrospectively on nine general anesthesia cases collected in the operating room. We utilize Shapley additive explanations to gain an understanding of the factors with the greatest influence over the agent's decision-making.

RESULTS

The deep RL agent significantly outperformed a proportional-integral-derivative controller (median episode median absolute performance error 1.9% ± 1.8 and 3.1% ± 1.1). The model that was rewarded for minimizing total doses performed the best across simulated patient demographics (median episode median performance error 1.1% ± 0.5). When run on real-world clinical datasets, the agent recommended doses that were consistent with those administered by the anesthesiologist.

CONCLUSIONS

The proposed approach marks the first fully continuous deep RL algorithm for automating anesthestic drug dosing. The reward function used by the RL training algorithm can be flexibly designed for desirable practices (e.g. use less anesthetic) and bolstered performances. Through careful analysis of the learned policies, techniques for interpreting dosing decisions, and testing on clinical data, we confirm that the agent's anesthetic dosing is consistent with our understanding of best-practices in anesthesia care.

Depth of Anesthesia Monitoring

The electroencephalogram (EEG) can be analyzed in its raw form for characteristic drug-induced patterns of change or summarized using mathematical parameters as a processed electroencephalogram (pEEG). In this article we aim to summarize the contemporary literature pertaining to the commonly available pEEG monitors including the effects of commonly used anesthetic drugs on the EEG and pEEG parameters, pEEG monitor pitfalls, and the clinical implications of pEEG monitoring for anesthesia, pediatrics, and intensive care.

Machine learning of EEG spectra classifies unconsciousness during GABAergic anesthesia

In current anesthesiology practice, anesthesiologists infer the state of unconsciousness without directly monitoring the brain. Drug- and patient-specific electroencephalographic (EEG) signatures of anesthesia-induced unconsciousness have been identified previously. We applied machine learning approaches to construct classification models for real-time tracking of unconscious state during anesthesia-induced unconsciousness. We used cross-validation to select and train the best performing models using 33,159 2s segments of EEG data recorded from 7 healthy volunteers who received increasing infusions of propofol while responding to stimuli to directly assess unconsciousness. Cross-validated models of unconsciousness performed very well when tested on 13,929 2s EEG segments from 3 left-out volunteers collected under the same conditions (median volunteer AUCs 0.99-0.99). Models showed strong generalization when tested on a cohort of 27 surgical patients receiving solely propofol collected in a separate clinical dataset under different circumstances and using different hardware (median patient AUCs 0.95-0.98), with model predictions corresponding with actions taken by the anesthesiologist during the cases. Performance was also strong for 17 patients receiving sevoflurane (alone or in addition to propofol) (median AUCs 0.88-0.92). These results indicate that EEG spectral features can predict unconsciousness, even when tested on a different anesthetic that acts with a similar neural mechanism. With high performance predictions of unconsciousness, we can accurately monitor anesthetic state, and this approach may be used to engineer infusion pumps to intelligibly respond to patients' neural activity.

The Effect of Low-Dose Intraoperative Ketamine on Closed-Loop-Controlled General Anesthesia: A Randomized Controlled Equivalence Trial

BACKGROUND

Closed-loop control of propofol-remifentanil anesthesia using the processed electroencephalography depth-of-hypnosis index provided by the NeuroSENSE monitor (WAVCNS) has been previously described. The purpose of this placebo-controlled study was to evaluate the performance (percentage time within ±10 units of the setpoint during the maintenance of anesthesia) of a closed-loop propofol-remifentanil controller during induction and maintenance of anesthesia in the presence of a low dose of ketamine.

METHODS

Following ethical approval and informed consent, American Society of Anesthesiologist (ASA) physical status I-II patients aged 19-54 years, scheduled for elective orthopedic surgery requiring general anesthesia for >60 minutes duration, were enrolled in a double-blind randomized, placebo-controlled, 2-group equivalence trial. Immediately before induction of anesthesia, participants in the ketamine group received a 0.25 mg·kg-1 bolus of intravenous ketamine over 60 seconds followed by a continuous 5 µg·kg-1·min-1 infusion for up to 45 minutes. Participants in the control group received an equivalent volume of normal saline. After the initial study drug bolus, closed-loop induction of anesthesia was initiated; propofol and remifentanil remained under closed-loop control until the anesthetic was tapered and turned off at the anesthesiologist's discretion. An equivalence range of ±8.99% was assumed for comparing controller performance.

RESULTS

Sixty patients participated: 41 males, 54 ASA physical status I, with a median (interquartile range [IQR]) age of 29 [23, 38] years and weight of 82 [71, 93] kg. Complete data were available from 29 cases in the ketamine group and 27 in the control group. Percentage time within ±10 units of the WAVCNS setpoint was median [IQR] 86.6% [79.7, 90.2] in the ketamine group and 86.4% [76.5, 89.8] in the control group (median difference, 1.0%; 95% confidence interval [CI] -3.6 to 5.0). Mean propofol dose during maintenance of anesthesia for the ketamine group was higher than for the control group (median difference, 24.9 µg·kg-1·min-1; 95% CI, 6.5-43.1; P = .005).

CONCLUSIONS

Because the 95% CI of the difference in controller performance lies entirely within the a priori equivalence range, we infer that this analgesic dose of ketamine did not alter controller performance. Further study is required to confirm the finding that mean propofol dosing was higher in the ketamine group, and to investigate the implication that this dose of ketamine may have affected the WAVCNS.

Wavelet and pain rating index for inhalation anesthesia: A randomized controlled trial

BACKGROUND

Wavelet index (WLi) and pain rating index (PRi) are new parameters for regulating general anesthesia depth based on wavelet analysis.

AIM

To investigate the safety and efficacy of using WLi or PRi in sevoflurane anesthesia.

METHODS

This randomized controlled trial enrolled 66 patients scheduled for elective posterior lumbar interbody fusion surgery under sevoflurane anesthesia between September 2017 and February 2018. A random number generator was used to assign the eligible patients to three groups: Systolic blood pressure (SBP) monitoring group, WLi monitoring group, and PRi monitoring group. The main anesthesiologist was aware of the patient grouping and intervention used. The primary endpoint was anesthesia recovery time. Secondary endpoints included extubation time, sevoflurane consumption, number of unwanted events/ interventions, number of adverse events and postoperative visual analogue scale for pain.

RESULTS

A total of 62 patients were included in the final analysis (SBP group, n = 21; WLi group, n = 21; and PRi group, n = 20). There were no significant differences among the three groups in patient age, gender distribution, body mass index, American Society of Anesthesiologists class, duration of surgery, or duration of anesthesia. Anesthesia recovery time was shorter in the WLi and PRi groups than in the SBP group with no significant difference between the WLi and PRi groups. Extubation time was shorter in the WLi and PRi groups than in the SBP group. Sevoflurane consumption was lower in the WLi and PRi groups than in the SBP group. Nicardipine was more commonly needed to treat hypertension in the WLi and PRi groups than in the SBP group.

CONCLUSION

Regulation of sevoflurane anesthesia depth with WLi or PRi reduced anesthesia recovery time, extubation time and sevoflurane consumption without intraoperative unwanted events.

The Raw and Processed Electroencephalogram as a Monitoring and Diagnostic Tool

In this narrative review, different aspects of electroencephalogram (EEG) monitoring during anesthesia are approached, with a special focus on cardiothoracic and vascular anesthesia, from the basic principles to more sophisticated diagnosis and monitoring utilities. The available processed EEG-derived indexes of the depth of the hypnotic component of anesthesia have well-defined limitations and usefulness. They prevent intraoperative awareness with recall in specific patient populations and under a specific anesthetic regimen. They prevent intraoperative overdose, and they shorten recovery times. They also help to avoid lengthy intraoperative periods of suppression activity, which are known to be deleterious in terms of outcome. Other than those available indexes, the huge amount of information contained in the EEG currently is being used only partially. Several other areas of interest regarding EEG during anesthesia have emerged in terms of anesthesia mechanisms elucidation, nociception monitoring, and diagnosis or prevention of brain insults.

Individualized closed-loop anesthesia through patient model partitioning

Closed-loop controlled drug dosing has the potential of revolutionizing clinical anesthesia. However, inter-patient variability in drug sensitivity poses a central challenge to the synthesis of safe controllers. Identifying a full individual pharmacokinetic-pharmacodynamic (PKPD) model for this synthesis is clinically infeasible due to limited excitation of PKPD dynamics and presence of unmodeled disturbances. This work presents a novel method to mitigate inter-patient variability. It is based on: 1) partitioning an a priori known model set into subsets; 2) synthesizing an optimal robust controller for each subset; 3) classifying patients into one of the subsets online based on demographic or induction phase data; 4) applying the associated closed-loop controller. The method is investigated in a simulation study, utilizing a set of 47 clinically obtained patient models. Results are presented and discussed.Clinical relevance-The proposed method is easy to implement in clinical practice, and has potential to reduce the impact from surgical stimulation disturbances, and to result in safer closed-loop anesthesia with less risk of under- and over dosing.

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.

Effect of ketamine on the NeuroSENSE WAVCNS during propofol anesthesia; a randomized feasibility trial

Dose-dependent effects of ketamine on processed electroencephalographic depth-of-hypnosis indices have been reported. Limited data are available for the NeuroSENSE WAV_CNS index. Our aim was to establish the feasibility of closed-loop propofol-remifentanil anesthesia guided by the WAV_CNS index in the presence of an analgesic dose of ketamine. Thirty ASA I-II adults, 18-54 years, requiring general anesthesia for anterior cruciate ligament surgery were randomized to receive: full-dose [ketamine, 0.5 mg kg^-1 initial bolus, 10 mcg kg^-1 min^-1 infusion] (recommended dose for postoperative pain management); half-dose [ketamine, 0.25 mg kg^-1 bolus, 5 mcg kg^-1 min^-1 infusion]; or control [no ketamine]. After the ketamine bolus, patients received 1.0 mcg kg^-1 remifentanil over 30 s, then 1.5 mg kg^-1 propofol over 30 s, followed by manually-adjusted propofol-remifentanil anesthesia. The WAV_CNS was > 60 for 7/9 patients in the full-dose group at 7 min after starting the propofol infusion. This was inconsistent with clinical observations of depth-of-hypnosis and significantly higher than control (median difference [MD] 17.0, 95% confidence interval [CI] 11.4-26.8). WAV_CNS was median [interquartile range] 49.3 [42.2-62.6] in the half-dose group, and not different to control (MD 5.1, 95% CI - 4.9 to 17.9). During maintenance of anesthesia, the WAV_CNS was higher in the full-dose group compared to control (MD 14.7, 95% CI 10.2-19.2) and in the half-dose group compared to control (MD 11.4, 95% CI 4.7-20.4). The full-dose of ketamine recommended for postoperative pain management had a significant effect on the WAV_CNS. This effect should be considered when using the WAV_CNS to guide propofol-remifentanil dosing.Trial Registration ClinicalTrails.gov No. NCT02908945.

An Autonomous Alarm System for Personal Safety Assurance of Intimate Partner Violence Survivors Based on Passive Continuous Monitoring through Biosensors

Intimate Partner Violence (IPV) dramatically compromises the free and complete development of many women around the world, therefore leading to social asymmetry regarding the right to personal safety. In many cases, a woman who has reported her partner to police for gender-based violence needs to ensure her protection (either before the trial of the aggressor or after their freedom). Thus, it would be ideal if autonomous alarm systems could be developed in order to call the police if necessary. Up to now, many proposals have been presented in this regard, including solutions based on Information and Communication Technologies (ICT) but, unfortunately, these approaches usually rely on the active participation of the victims (survivors), who have to turn the system on by themselves if needed. Therefore, in order to overcome such limitations, in this work, a passive continuous monitoring system is proposed which uses biosensors attached to the survivor as well as machine learning techniques to infer if an abnormal situation related to gender-based violence is taking place, activating in this case an alarm. The monitoring structure of the system supervises a great deal of bio-signals according to the current status of technology of wearables and biomedical devices. The presented biosensors-based surveillance solution can also be manually disconnected for 30/60/90 min (on demand) in order to avoid false positives when a woman is, for example, practicing sports or carrying out other inoffensive activities that could incorrectly activate the alarm.

Pain Detection with Bioimpedance Methodology from 3-Dimensional Exploration of Nociception in a Postoperative Observational Trial

Although the measurement of dielectric properties of the skin is a long-known tool for assessing the changes caused by nociception, the frequency modulated response has not been considered yet. However, for a rigorous characterization of the biological tissue during noxious stimulation, the bioimpedance needs to be analyzed over time as well as over frequency. The 3-dimensional analysis of nociception, including bioimpedance, time, and frequency changes, is provided by ANSPEC-PRO device. The objective of this observational trial is the validation of the new pain monitor, named as ANSPEC-PRO. After ethics committee approval and informed consent, 26 patients were monitored during the postoperative recovery period: 13 patients with the in-house developed prototype ANSPEC-PRO and 13 with the commercial device MEDSTORM. At every 7 min, the pain intensity was measured using the index of Anspec-pro or Medstorm and the 0-10 numeric rating scale (NRS), pre-surgery for 14 min and post-anesthesia for 140 min. Non-significant differences were reported for specificity-sensitivity analysis between ANSPEC-PRO (AUC = 0.49) and MEDSTORM (AUC = 0.52) measured indexes. A statistically significant positive linear relationship was observed between Anspec-pro index and NRS (r2 = 0.15, p < 0.01). Hence, we have obtained a validation of the prototype Anspec-pro which performs equally well as the commercial device under similar conditions.

Evaluating NeuroSENSE for assessing depth of hypnosis during desflurane anesthesia: an adaptive, randomized-controlled trial

PURPOSE

Processed electroencephalography (EEG) monitors support depth-of-hypnosis assessment during anesthesia. This randomized-controlled trial investigated the performance of the NeuroSENSE electroencephalography (EEG) monitor to determine whether its wavelet anesthetic value for central nervous system (WAVCNS) index distinguishes consciousness from unconsciousness during induction of anesthesia (as assessed by the anesthesiologist) and emergence from anesthesia (indicated by patient responsiveness), and whether it correlates with changes in desflurane minimum alveolar concentration (MAC) during maintenance of anesthesia.

METHODS

EEG was collected using a fronto-temporal bilateral montage. The WAVCNS was continuously recorded by the NeuroSENSE monitor, to which the anesthesiologist was blinded. Anesthesia was induced with propofol/remifentanil and maintained with desflurane, with randomized changes of -0.4, 0, or +0.4 MAC every 7.5 min within the 0.8-1.6 MAC range, if clinically acceptable to the anesthesiologist. During emergence from anesthesia, desflurane was stepped down by 0.2 MAC every five minutes.

RESULTS

Data from 75 patients with a median [interquartile range] age of 41[35-52] yr were obtained. The WAVCNS distinguished consciousness from unconsciousness as assessed by the anesthesiologist, with area under the receiver operating characteristic curve of 99.5% (95% confidence interval [CI], 98.5 to 100.0) at loss of consciousness and 99.4% (95% CI, 98.5 to 100.0) at return of consciousness. Bilateral WAVCNS changes correlated with desflurane concentrations, with -8.0 and -8.6 WAVCNS units, respectively, per 1 MAC change in the 0.8-1.6 MAC range during maintenance of anesthesia and -10.0 and -10.5 WAVCNS units, respectively, in the 0.4-1.6 MAC range including emergence from anesthesia.

CONCLUSION

The NeuroSENSE monitor can reliably discriminate between consciousness and unconsciousness, as assessed by the anesthesiologist, during induction of anesthesia and with a lower level of reliability during emergence from anesthesia. The WAVCNS correlates with desflurane concentration but plateaus at higher concentrations, similar to other EEG monitors, which suggests limited utility to titrate higher concentrations of anesthetic vapour.

TRIAL REGISTRATION

clinicaltrials.gov, NCT02088671; registered 17 March, 2014.

Non-linear Entropy Analysis in EEG to Predict Treatment Response to Repetitive Transcranial Magnetic Stimulation in Depression

Background: Biomarkers that predict clinical outcomes in depression are essential for increasing the precision of treatments and clinical outcomes. The electroencephalogram (EEG) is a non-invasive neurophysiological test that has promise as a biomarker sensitive to treatment effects. The aim of our study was to investigate a novel non-linear index of resting state EEG activity as a predictor of clinical outcome, and compare its predictive capacity to traditional frequency-based indices.

Methods: EEG was recorded from 62 patients with treatment resistant depression (TRD) and 25 healthy comparison (HC) subjects. TRD patients were treated with excitatory repetitive transcranial magnetic stimulation (rTMS) to the dorsolateral prefrontal cortex (DLPFC) for 4 to 6 weeks. EEG signals were first decomposed using the empirical mode decomposition (EMD) method into band-limited intrinsic mode functions (IMFs). Subsequently, Permutation Entropy (PE) was computed from the obtained second IMF to yield an index named PEIMF2. Receiver Operator Characteristic (ROC) curve analysis and ANOVA test were used to evaluate the efficiency of this index (PEIMF2) and were compared to frequency-band based methods.

Results: Responders (RP) to rTMS exhibited an increase in the PEIMF2 index compared to non-responders (NR) at F3, FCz and FC3 sites (p < 0.01). The area under the curve (AUC) for ROC analysis was 0.8 for PEIMF2 index for the FC3 electrode. The PEIMF2 index was superior to ordinary frequency band measures.

Conclusion: Our data show that the PEIMF2 index, yields superior outcome prediction performance compared to traditional frequency band indices. Our findings warrant further investigation of EEG-based biomarkers in depression; specifically entropy indices applied in band-limited EEG components. Registration in ClinicalTrials.Gov; identifiers NCT02800226 and NCT01887782.

Feasibility of continuous sedation monitoring in critically ill intensive care unit patients using the NeuroSENSE WAVCNS index

Sedation in the intensive care unit (ICU) is challenging, as both over- and under-sedation are detrimental. Current methods of assessment, such as the Richmond Agitation Sedation Scale (RASS), are measured intermittently and rely on patients' behavioral response to stimulation, which may interrupt sleep/rest. A non-stimulating method for continuous sedation monitoring may be beneficial and allow more frequent assessment. Processed electroencephalography (EEG) monitors have not been routinely adopted in the ICU. The aim of this observational study was to assess the feasibility of using the NeuroSENSE™ monitor for EEG-based continuous sedation assessment. With ethical approval, ICU patients on continuous propofol sedation were recruited. Depth-of-hypnosis index (WAV_CNS) values were obtained from the NeuroSENSE. Bedside nurses, blinded to the NeuroSENSE, performed regular RASS assessments and maintained the sedation regimen as per standard of care. Participants were monitored throughout the duration of their propofol infusion, up to 24 h. Fifteen patients, with median [interquartile range] age of 57 [52-62.5] years were each monitored for a duration of 9.0 [5.7-20.1] h. Valid WAV_CNS values were obtained for 89% [66-99] of monitoring time and were widely distributed within and between individuals, with 6% [1-31] spent < 40 (very deep), and 3% [1-15] spent > 90 (awake). Significant EEG suppression was detected in 3/15 (20%) participants. Observed RASS matched RASS goals in 36/89 (40%) assessments. The WAV_CNS variability, and incidence of EEG suppression, highlight the limitations of using RASS as a standalone sedation measure, and suggests potential benefit of adjunct continuous brain monitoring.

Event-Based control of depth of hypnosis in anesthesia

BACKGROUND AND OBJECTIVE

In this paper, we propose the use of an event-based control strategy for the closed-loop control of the depth of hypnosis in anesthesia by using propofol administration and the bispectral index as a controlled variable.

METHODS

A new event generator with high noise-filtering properties is employed in addition to a PIDPlus controller. The tuning of the parameters is performed off-line by using genetic algorithms by considering a given data set of patients.

RESULTS

The effectiveness and robustness of the method is verified in simulation by implementing a Monte Carlo method to address the intra-patient and inter-patient variability. A comparison with a standard PID control structure shows that the event-based control system achieves a reduction of the total variation of the manipulated variable of 93% in the induction phase and of 95% in the maintenance phase.

CONCLUSIONS

The use of event based automatic control in anesthesia yields a fast induction phase with bounded overshoot and an acceptable disturbance rejection. A comparison with a standard PID control structure shows that the technique effectively mimics the behavior of the anesthesiologist by providing a significant decrement of the total variation of the manipulated variable.

Optimized PID control of depth of hypnosis in anesthesia

BACKGROUND AND OBJECTIVE

This paper addresses the use of proportional-integral-derivative controllers for regulating the depth of hypnosis in anesthesia by using propofol administration and the bispectral index as a controlled variable. In fact, introducing an automatic control system might provide significant benefits for the patient in reducing the risk for under- and over-dosing.

METHODS

In this study, the controller parameters are obtained through genetic algorithms by solving a min-max optimization problem. A set of 12 patient models representative of a large population variance is used to test controller robustness. The worst-case performance in the considered population is minimized considering two different scenarios: the induction case and the maintenance case.

RESULTS

Our results indicate that including a gain scheduling strategy enables optimal performance for induction and maintenance phases, separately. Using a single tuning to address both tasks may results in a loss of performance up to 102% in the induction phase and up to 31% in the maintenance phase. Further on, it is shown that a suitably designed low-pass filter on the controller output can handle the trade-off between the performance and the noise effect in the control variable.

CONCLUSIONS

Optimally tuned PID controllers provide a fast induction time with an acceptable overshoot and a satisfactory disturbance rejection performance during maintenance. These features make them a very good tool for comparison when other control algorithms are developed.

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.

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.

Analysis of EEG to quantify depth of anesthesia using Hidden Markov Model

Real-time quantification of the patient's consciousness level during anesthesia is an important issue to avoid intraoperative awareness and post-operative side effects. A depth-of-anesthesia (DoA) monitoring method called Bispectral Index (BIS) is generally used for this purpose. However, BIS is known to be inaccurate at the transitory state, and also shows a critical time delay in quantifying the patient's consciousness level. This paper introduces a novel method to reduce the response time in the quantification process. This thesis develops a new index called HDoA by analyzing EEG using Hidden Markov Model. The proposed approach is composed by two steps, training and testing. In the training step, two HMM, awakened and anesthetized model are learned based on each training set. In the testing step, by evaluating the probability of producing the testing EEG from two models respectively, the index HDoA is derived. Since the evaluation of DoA using HMM is training based method, it have better performance with more training process. Experiments show that HDoA has a high correlation with BIS at a steady state, and outperforms BIS in two ways: (1) shorter delay time in transition state, and (2) higher Fisher Score. The validity of HDoA has been tested by 8 real clinical data.

Circadian variation of scalp EEG: a novel measure based on wavelet packet transform and differential entropy

We propose a novel entropy-based measure to quantify the circadian variations of scalp electroencephalogram (EEG) by analyzing waking epochs of nap opportunities under an ultradian sleep-wake cycle (USW) protocol. To compute this circadian measure for a nap opportunity, each waking epoch (~1 sec) is decomposed using wavelet packet transform and the relative energy for the desired frequency band (here, 10-12 Hz) is calculated. Then, in a bootstrapping procedure, a shape statistic (skewness or kurtosis) of the relative energy distribution, after each resampling, is computed. Finally, the probability density function of this statistic is estimated, and the corresponding differential entropy is considered as the circadian measure. This measure was evaluated using EEG recordings from 4 healthy subjects during a 72-h USW procedure. According to the results, the proposed measure showed a significant circadian variation both for individual and group data, with peak values occurring near the core body temperature minimum. The performance of the entropy-based measure was also compared with that of two other measures, namely mean energy logarithm and mean energy ratio, revealing the superiority of this measure.

Identification of scalp EEG circadian variation using a novel correlation sum measure

OBJECTIVE

In this paper, we propose a novel method to determine the circadian variation of scalp electroencephalogram (EEG) in both individual and group levels using a correlation sum measure, quantifying self-similarity of the EEG relative energy across waking epochs.

APPROACH

We analysed EEG recordings from central-parietal and occipito-parietal montages in nine healthy subjects undergoing a 72 h ultradian sleep-wake cycle protocol. Each waking epoch (∼ 1 s) of every nap opportunity was decomposed using the wavelet packet transform, and the relative energy for that epoch was calculated in the desired frequency band using the corresponding wavelet coefficients. Then, the resulting set of energy values was resampled randomly to generate different subsets with equal number of elements. The correlation sum of each subset was then calculated over a range of distance thresholds, and the average over all subsets was computed. This average value was finally scaled for each nap opportunity and considered as a new circadian measure.

MAIN RESULTS

According to the evaluation results, a clear circadian rhythm was identified in some EEG frequency ranges, particularly in 4-8 Hz and 10-12 Hz. The correlation sum measure not only was able to disclose the circadian rhythm on the group data but also revealed significant circadian variations in most individual cases, as opposed to previous studies only reporting the circadian rhythms on a population of subjects. Compared to a naive measure based on the EEG absolute energy in the frequency band of interest, the proposed measure showed a clear superiority using both individual and group data. Results also suggested that the acrophase (i.e., the peak) of the circadian rhythm in 10-12 Hz occurs close to the core body temperature minimum.

SIGNIFICANCE

These results confirm the potential usefulness of the proposed EEG-based measure as a non-invasive circadian marker.

Safety-preserving closed-loop control of anesthesia

In safety-critical control systems, such as closed-loop control of anesthesia, it is of utmost importance to guarantee the ability of a control approach to maintain states of the systems within a safe region. In this paper, we address the problem by applying a safety-preserving control technique to anesthesia control. The approach relies on a conservative approximation of the viability set. The set specifies initial states for which there exists an input that keeps the trajectory emanating from those states within the safe region. This approach can be applied to any type of controller which satisfies the performance criteria. Furthermore, it prevents the performance controller from taking the states out of the safe region.

Robust control of burst suppression for medical coma

OBJECTIVE

Medical coma is an anesthetic-induced state of brain inactivation, manifest in the electroencephalogram by burst suppression. Feedback control can be used to regulate burst suppression, however, previous designs have not been robust. Robust control design is critical under real-world operating conditions, subject to substantial pharmacokinetic and pharmacodynamic parameter uncertainty and unpredictable external disturbances. We sought to develop a robust closed-loop anesthesia delivery (CLAD) system to control medical coma.

APPROACH

We developed a robust CLAD system to control the burst suppression probability (BSP). We developed a novel BSP tracking algorithm based on realistic models of propofol pharmacokinetics and pharmacodynamics. We also developed a practical method for estimating patient-specific pharmacodynamics parameters. Finally, we synthesized a robust proportional integral controller. Using a factorial design spanning patient age, mass, height, and gender, we tested whether the system performed within clinically acceptable limits. Throughout all experiments we subjected the system to disturbances, simulating treatment of refractory status epilepticus in a real-world intensive care unit environment.

MAIN RESULTS

In 5400 simulations, CLAD behavior remained within specifications. Transient behavior after a step in target BSP from 0.2 to 0.8 exhibited a rise time (the median (min, max)) of 1.4 [1.1, 1.9] min; settling time, 7.8 [4.2, 9.0] min; and percent overshoot of 9.6 [2.3, 10.8]%. Under steady state conditions the CLAD system exhibited a median error of 0.1 [-0.5, 0.9]%; inaccuracy of 1.8 [0.9, 3.4]%; oscillation index of 1.8 [0.9, 3.4]%; and maximum instantaneous propofol dose of 4.3 [2.1, 10.5] mg kg(-1). The maximum hourly propofol dose was 4.3 [2.1, 10.3] mg kg(-1) h(-1). Performance fell within clinically acceptable limits for all measures.

SIGNIFICANCE

A CLAD system designed using robust control theory achieves clinically acceptable performance in the presence of realistic unmodeled disturbances and in spite of realistic model uncertainty, while maintaining infusion rates within acceptable safety limits.

Prediction of Nociceptive Responses during Sedation by Linear and Non-Linear Measures of EEG Signals in High Frequencies

The level of sedation in patients undergoing medical procedures evolves continuously, affected by the interaction between the effect of the anesthetic and analgesic agents and the pain stimuli. The monitors of depth of anesthesia, based on the analysis of the electroencephalogram (EEG), have been progressively introduced into the daily practice to provide additional information about the state of the patient. However, the quantification of analgesia still remains an open problem. The purpose of this work is to improve the prediction of nociceptive responses with linear and non-linear measures calculated from EEG signal filtered in frequency bands higher than the traditional bands. Power spectral density and auto-mutual information function was applied in order to predict the presence or absence of the nociceptive responses to different stimuli during sedation in endoscopy procedure. The proposed measures exhibit better performances than the bispectral index (BIS). Values of prediction probability of Pk above 0.75 and percentages of sensitivity and specificity above 70% were achieved combining EEG measures from the traditional frequency bands and higher frequency bands.

Feasibility of Closed-loop Titration of Propofol and Remifentanil Guided by the Bispectral Monitor in Pediatric and Adolescent Patients

Background:

This study was designed to assess the feasibility of dual closed-loop titration of propofol and remifentanil guided solely by the Bispectral Index (BIS) monitor in pediatric and adolescent patients during anesthesia.

Methods:

Children undergoing elective surgery in this single-blind randomized study were allocated into the closed-loop (auto) or manual (manual) group. Primary outcome was the percentage of time with the BIS in the range 40 to 60 (BIS40–60). Secondary outcomes were the percentage of deep (BIS&lt;40) anesthesia and drug consumption. Data are presented as median (interquartile range) or number (%).

Results:

Twenty-three patients (12 [10 to 14] yr) were assigned to the auto group and 19 (14 [7 to 14] yr) to the manual group. The closed-loop controller was able to provide induction and maintenance for all patients. The percentage of time with BIS40–60 was greater in the auto group (87% [75 to 96] vs. 72% [48 to 79]; P = 0.002), with a decrease in the percentage of BIS&lt;40 (7% [2 to 17] vs. 21% [11 to 38]; P = 0.002). Propofol (2.4 [1.9 to 3.3] vs. 1.7 [1.2 to 2.8] mg/kg) and remifentanil (2.3 [2.0 to 3.0] vs. 2.5 [1.2 to 4.3] μg/kg) consumptions were similar in auto versus manual groups during induction, respectively. During maintenance, propofol consumption (8.2 [6.0 to 10.2] vs. 7.9 [7.2 to 9.1] mg kg−1 h−1; P = 0.89) was similar between the two groups, but remifentanil consumption was greater in the auto group (0.39 [0.22 to 0.60] vs. 0.22 [0.17 to 0.32] μg kg−1 min−1; P = 0.003). Perioperative adverse events and length of stay in the postanesthesia care unit were similar.

Conclusion:

Intraoperative automated control of hypnosis and analgesia guided by the BIS is clinically feasible in pediatric and adolescent patients and outperformed skilled manual control.

Reduction of the dimensionality of the EEG channels during scoliosis correction surgeries using a wavelet decomposition technique

This paper presents a comparison between the electroencephalogram (EEG) channels during scoliosis correction surgeries. Surgeons use many hand tools and electronic devices that directly affect the EEG channels. These noises do not affect the EEG channels uniformly. This research provides a complete system to find the least affected channel by the noise. The presented system consists of five stages: filtering, wavelet decomposing (Level 4), processing the signal bands using four different criteria (mean, energy, entropy and standard deviation), finding the useful channel according to the criteria's value and, finally, generating a combinational signal from Channels 1 and 2. Experimentally, two channels of EEG data were recorded from six patients who underwent scoliosis correction surgeries in the Pusat Perubatan Universiti Kebangsaan Malaysia (PPUKM) (the Medical center of National University of Malaysia). The combinational signal was tested by power spectral density, cross-correlation function and wavelet coherence. The experimental results show that the system-outputted EEG signals are neatly switched without any substantial changes in the consistency of EEG components. This paper provides an efficient procedure for analyzing EEG signals in order to avoid averaging the channels that lead to redistribution of the noise on both channels, reducing the dimensionality of the EEG features and preparing the best EEG stream for the classification and monitoring stage.

A stationary wavelet transform based approach to registration of planning CT and setup cone beam-CT images in radiotherapy

Image registration between planning CT images and cone beam-CT (CBCT) images is one of the key technologies of image guided radiotherapy (IGRT). Current image registration methods fall roughly into two categories: geometric features-based and image grayscale-based. Mutual information (MI) based registration, which belongs to the latter category, has been widely applied to multi-modal and mono-modal image registration. However, the standard mutual information method only focuses on the image intensity information and overlooks spatial information, leading to the instability of intensity interpolation. Due to its use of positional information, wavelet transform has been applied to image registration recently. In this study, we proposed an approach to setup CT and cone beam-CT (CBCT) image registration in radiotherapy based on the combination of mutual information (MI) and stationary wavelet transform (SWT). Firstly, SWT was applied to generate gradient images and low frequency components produced in various levels of image decomposition were eliminated. Then inverse SWT was performed on the remaining frequency components. Lastly, the rigid registration of gradient images and original images was implemented using a weighting function with the normalized mutual information (NMI) being the similarity measure, which compensates for the lack of spatial information in mutual information based image registration. Our experiment results showed that the proposed method was highly accurate and robust, and indicated a significant clinical potential in improving the accuracy of target localization in image guided radiotherapy (IGRT).

How electroencephalography serves the anesthesiologist

Major clinical endpoints of general anesthesia, such as the alteration of consciousness, are achieved through effects of anesthetic agents on the central nervous system, and, more precisely, on the brain. Historically, clinicians and researchers have always been interested in quantifying and characterizing those effects through recordings of surface brain electrical activity, namely electroencephalography (EEG). Over decades of research, the complex signal has been dissected to extract its core substance, with significant advances in the interpretation of the information it may contain. Methodological, engineering, statistical, mathematical, and computer progress now furnishes advanced tools that not only allow quantification of the effects of anesthesia, but also shed light on some aspects of anesthetic mechanisms. In this article, we will review how advanced EEG serves the anesthesiologist in that respect, but will not review other intraoperative utilities that have no direct relationship with consciousness, such as monitoring of brain and spinal cord integrity. We will start with a reminder of anesthestic effects on raw EEG and its time and frequency domain components, as well as a summary of the EEG analysis techniques of use for the anesthesiologist. This will introduce the description of the use of EEG to assess the depth of the hypnotic and anti-nociceptive components of anesthesia, and its clinical utility. The last part will describe the use of EEG for the understanding of mechanisms of anesthesia-induced alteration of consciousness. We will see how, eventually in association with transcranial magnetic stimulation, it allows exploring functional cerebral networks during anesthesia. We will also see how EEG recordings during anesthesia, and their sophisticated analysis, may help corroborate current theories of mental content generation.

Lessons learned from closed loops in engineering: towards a multivariable approach regulating depth of anaesthesia

In this paper is presented a brief state of art regarding the multivariable formulation for controlling the depth of anaesthesia by means of two intravenously administrated drugs, i.e. propofol and remifentanil. In a feasibility study of determining a suitable variable to quantify analgesia levels in patients undergoing cardiac surgery, the bispectral index and an electromyogram-based surrogate variable are proposed as the controlled variables. The study is carried on in the context of implementing a multivariable predictive control algorithm. The simulation results show that such a paradigm is feasible, although it does not guarantee perfect knowledge of the analgesia level-in other words, the variable is not validated against typical evaluations of the pain levels (e.g. clinical scores).