Depth-of-anaesthesia monitoring

All anaesthetists would like to be confident that their patients are asleep throughout surgery. Depth-of-anaesthesia monitors may contribute to reducing the incidence of perioperative awareness, but they are expensive, and typically require that consumables are purchased for every case.Recently, excessive depth of anaesthesia has been feebly associated with increased mortality, but this has not yet been proven, and may reflect patient co-morbidity, rather than clinician error

An improved chaos method for monitoring the depth of anaesthesia

This paper proposed a new method to monitor the depth of anaesthesia (DoA) by modifying the Hurst parameters in Chaos method. Two new indices (CDoA and CsDoA) are proposed to estimate the anaesthesia states of patients. In order to reduce the fluctuation of CDoA and CsDoA trends, the Chaos and Modified Detrended Average methods (C-MDMA) are combined together. Compared with Bispectrum (BIS) index, CDoA, the CsDoA and C-MDMA trends are close to the BIS trend in the whole scale from 100 to 0 with a full recording time

Monitoring the Depth of Anaesthesia

One of the current challenges in medicine is monitoring the patients’ depth of general anaesthesia (DGA). Accurate assessment of the depth of anaesthesia contributes to tailoring drug administration to the individual patient, thus preventing awareness or excessive anaesthetic depth and improving patients’ outcomes. In the past decade, there has been a significant increase in the number of studies on the development, comparison and validation of commercial devices that estimate the DGA by analyzing electrical activity of the brain (i.e., evoked potentials or brain waves). In this paper we review the most frequently used sensors and mathematical methods for monitoring the DGA, their validation in clinical practice and discuss the central question of whether these approaches can, compared to other conventional methods, reduce the risk of patient awareness during surgical procedures

Chaos-modified detrended moving average methodology for monitoring the depth of anaesthesia

This paper proposes a new method to monitor the depth of anaesthesia (DoA) based on the EEG signal. This approach firstly uses discrete wavelet transform (DWT) to to remove the spikes and the low frequency noise from raw EEG signals. After de-noising the EEG signals, the modified Hurst parameter is proposed with two new indices (CDoA and CsDoA), to estimate the anaesthesia states of the patients. To reduce the fluctuation of the new DoA index, a combination of Modified Chaos and Modifying Detrended Moving Average is used (MC-DMA). Analyses of variance (ANOVA) for C-MDMA and BIS distributions are presented The results indicate that the C-MDMA distributions at each anaesthesia state level are significantly different and the C-MDMA can distinguish five depths of anaesthesia. Compared with BIS trends, MC-DMA trend is close to BIS trend covering the whole scale from 100 to 0 with a full recording time

Correlation of Sedline-generated variables and clinical signs with anaesthetic depth in experimental pigs receiving propofol.

Most of currently available electroencephalographic (EEG)-based tools to assess depth of anaesthesia have not been studied or have been judged unreliable in pigs. Our primary aim was to investigate the dose-effect relationship between increasing propofol dose and variables generated by the EEG-based depth of anaesthesia monitor Sedline in pigs. A secondary aim was to compare the anaesthetic doses with clinical outcomes commonly used to assess depth of anaesthesia in this species. Sixteen juvenile pigs were included. Propofol infusion was administered at 10 mg kg-1 h-1, increased by 10 mg kg-1 h-1 every 15 minutes, and stopped when an EEG Suppression ratio >80% was reached. Patient state index, suppression ratio, left and right spectral edge frequency 95%, and outcomes from commonly used clinical methods to assess depth of anaesthesia in pigs were recorded. The best pharmacodynamic model was assessed for Patient state index, suppression ratio, left and right spectral edge frequency 95% in response to propofol administration. The decrease of Patient state index best fitted to an inhibitory double-sigmoid model (including a plateau phase). The increase of suppression ratio fitted a typical sigmoid Emax model. No relevant relationship could be identified between spectral edge frequency 95% values and propofol administration. A large variability in clinical outcomes was observed among pigs, such that they did not provide a reliable evaluation of propofol dose. The relationship between propofol dose and Patient state index/suppression ratio described in the present study can be used for prediction in future investigations. The evaluation of depth of anaesthesia based on common clinical outcomes was not reliable

Robust internal model control for depth of anaesthesia

This paper investigates the depth of anaesthesia control problem during a surgery, where paralytic, analgesic and hypnotic are regulated by means of monitored administration of specific drugs. A robust internal model controller (RIMC) based on the Bispectral Index (BIS) is proposed. The controller compares the measured BIS with its input reference to provide the expected propofol concentration, and then the controller manipulates the anaesthetic propofol concentration entering the anaesthetic system to achieve the desired BIS value. This study develops patient dose-response models and provides an adequate drug administration regimen to avoid under or over dosing of patients. Numerical simulations illustrate that the RIMC performed better than the traditional PID controller. The robust performance of the two controllers is evaluated for a wide range of patient models by varying in patient parameters. The other relative performance is also compared for different BIS step settings

Spectral edge frequency during general anaesthesia: A narrative literature review

Previous studies have attempted to determine the depth of anaesthesia with different anaesthetic agents using electroencephalogram (EEG) measurements with variable success. Measuring depth of anaesthesia is confounded by the complexity of the EEG and the fact that different agents create different pattens. A narrative review was undertaken to examine the available research evidence on the effect and reliability of spectral edge frequency (SEF) for assessing the depth of anaesthesia in adult patients under general anaesthesia. A systematic search of the PubMed®, Scopus®, CINAHL and Cochrane databases identified six randomized controlled trials and five observational studies. The findings of these studies suggest that SEF varies according to the anaesthetic drugs used. Remifentanil and age are two factors that can affect SEF, while other opioids and benzodiazepine (administered separately) seem to have no effect. No patients experienced intraoperative awareness. However, this does not indicate that SEF can provide full protection against it and the number of articles in which intraoperative awareness was studied was too small to afford any certainty. None of the studies demonstrated a reliable SEF interval associated with adequate general anaesthesia. SEF must be adapted to the anaesthetic drug used, the patient’s age and state while under general anaesthesia

Depth of anesthesia control using internal model control techniques

The major difficulty in the design of closed-loop control during anaesthesia is the inherent patient variability due to differences in demographic and drug tolerance. These discrepancies are translated into the pharmacokinetics (PK), and pharmacodynamics (PD). These uncertainties may affect the stability of the closed loop control system. This paper aims at developing predictive controllers using Internal Model Control technique. This study develops patient dose-response models and to provide an adequate drug administration regimen for the anaesthesia to avoid under or over dosing of the patients. The controllers are designed to compensate for patients inherent drug response variability, to achieve the best output disturbance rejection, and to maintain optimal set point response. The results are evaluated compared with traditional PID controller and the performance is confirmed in our simulation

Diagnosis and decision-making for awareness during general anaesthesia

This is the post-print version of the article. The official published version can be obtained from the link below.We describe the design process of a diagnostic system for monitoring the anaesthetic state of patients during surgical interventions under general anaesthesia. Mid-latency auditory evoked potentials (MLAEPs) obtained during general anaesthesia are used to design a neuro-fuzzy system for the determination of the level of unconsciousness after feature extraction using multiresolution wavelet analysis (MRWA). The neuro-fuzzy system proves to be a useful tool in eliciting knowledge for the fuzzy system: the anaesthetist's expertise is indirectly coded in the knowledge rule-base through the learning process with the training data. The anaesthetic depth of the patient, as deduced by the anaesthetist from the clinical signs and other haemodynamic variables, noted down during surgery, is subsequently used to label the MLAEP data accordingly. This anaesthetist-labelled data, used to train the neuro-fuzzy system, is able to produce a classifier that successfully interprets unseen data recorded from other patients. This system is not limited, however, to the combination of drugs used here. Indeed, the similar effects of inhalational and analgesic anaesthetic drugs on the MLAEPs demonstrate that the system could potentially be used for any anaesthetic and analgesic drug combination. We also suggest the use of a closed-loop architecture that would automatically provide the drug profile necessary to maintain the patient at a safe level of sedation

Monitoring the depth of anaesthesia using simplified electroencephalogram (EEG)

Anaesthesia is administered routinely every day in hospitals and medical facilities. Numerous methods have been devised and implemented for monitoring the depth of anaesthesia (DoA) in order to guarantee the safety of patients. Monitoring the depth of anaesthesia provides anaesthesia professionals with an additional method to assess anaesthetic effects and patient responses during surgery. The measurement of depth of anaesthesia benefits patients and helps anaesthetists such as 'reduction in primary anaesthetic use, reduction in emergence and recovery time, improved patient satisfaction and decreased incidence of intra-operative awareness and recall' (Kelley S. D.). Clinical practice uses autonomic signs such as heart rate, blood pressure, pupils, tears, and sweating to determine depth of anaesthesia. However, clinical assessment of DoA is not valuable in predicting the response to a noxious stimulusand may vary depending on disease, drugs and surgical technique. Currently available DoA monitoring devices have been criticised in the literature, such as being redundant (Schneider, 2004), not responsive to some anaesthetic agents (Barr G., 1999), and time delay (Pilge S., 2006). This research proposes new methods to monitor the depth of anaesthesia (DoA) based on simplified EEG signals. These EEG signals were analysed in both the time domain and the time-frequency domain. In the time domain, the Detrended Fluctuation Analysis (DFA), detrended moving average (DMA) and Chaos methods are modified to study the scaling behaviour of the EEG as a measure of the DoA. In the frequency domain, fast Fourier transform (FFT) and filter bank are used to identify difference states of anaesthesia. In the time-frequency domain, discrete wavelet transforms (DWT) and power spectral density (PSD) function are applied to pre-process EEG data and to monitor the DoA. Firstly, a new de-noising algorithm is proposed with a threshold TWE, which is a function of wavelet entropy and the window length m for an EEG segment. Secondly, the anaesthesia states are identified into awake, light, moderate, deep and very deep anaesthesia states. Finally, the DoA indices are computed using: Modified DFA method (MDFA I), Modified DFA-Lagrange method (MDFA II), Modified detrended moving average method (MDMA), Modified Chaos method, combined Chaos and MDMA method, Wavelet-power spectral density. Simulation results demonstrate that our new methods monitor the DoA in all anaesthesia states accurately. These proposed methods and indices present a good responsive to anaesthetic agent, reduce the time delay when patient’s hypnotic state changes (from 12 to 178 seconds), and can estimate a patient’s hypnotic state when signal quality is poor