Identification of causal relations between haemodynamic variables, auditory evoked potentials and isoflurane by means of fuzzy logic

EEG under anesthesia--feature extraction with TESPAR

We investigated the problem of automatic depth of anesthesia (DOA) estimation from electroencephalogram (EEG) recordings. We employed Time Encoded Signal Processing And Recognition (TESPAR), a time-domain signal processing technique, in combination with multi-layer perceptrons to identify DOA levels. The presented system learns to discriminate between five DOA classes assessed by human experts whose judgements were based on EEG mid-latency auditory evoked potentials (MLAEPs) and clinical observations. We found that our system closely mimicked the behavior of the human expert, thus proving the utility of the method. Further analyses on the features extracted by our technique indicated that information related to DOA is mostly distributed across frequency bands and that the presence of high frequencies (> 80 Hz), which reflect mostly muscle activity, is beneficial for DOA detection.

Analysis of bispectral index and middle latency auditory-evoked potentials parameters in critically ill children

To study the parameters of the bispectral index and middle latency auditory-evoked potentials in critically ill children. We performed a prospective, observational study in 81 critically ill children categorized into two levels of sedation (moderate and deep) and three age groups (<1, 1-6, and >6 months). The parameters of the bispectral index and middle latency auditory-evoked potentials studied were the signal quality index, suppression ratio, total power, spectral edge frequency, electromyographic activity, and suppression ratio. Suppression rates were higher in children younger than 1 month than in older children and were also higher in deep sedation. Total power and spectral edge frequency increased with age. Electromyographic activity was higher in moderate sedation. Spectral edge frequency and suppression rates change with age and level of sedation. These variations are a consequence of the electroencephalographic structure at young ages and are secondary to the higher proportion of slow waves in deep states of sedation.

Comparison of auditory evoked potential parameters for predicting clinically anaesthetized state

BACKGROUND

Most of the research efforts to monitor the depth of anaesthesia using the mid-latency auditory evoked potential (MLAEP) signal in humans are based on the detection of the amplitudes and latencies of the signal peaks. Attempts have also been made to combine different time-domain and frequency-domain parameters. A comparison of different parameters is required to identify those which best discriminate the awake state from the anaesthetized state.

METHODS

Although the sensitivity of MLAEP signal peaks is appreciable in awake and light anaesthesia states, it is reduced considerably at the moderate anaesthesia level, rendering this method unsuitable for predicting the surgical stage of anaesthesia. To overcome this problem, a numerically derived quantity--the morphology index--was used which does not require location of the peaks of the signal, but, at the same time, reflects the changes in both the latency and amplitude of the peaks. AEPs were recorded in the hospital for 18 patients during various states, i.e. awake, induction, unconscious and after regaining consciousness from halothane anaesthesia. The peak latencies, amplitudes, morphology index and peak power frequency (PPF) were calculated.

RESULTS

The sensitivity and specificity of PPF (89% and 95%, respectively) were found to be better than those for Pa and Nb peak amplitudes, their latencies and the morphology index. In addition, PPF showed minimum inter-patient variation. The mean value (standard deviation) of this parameter was 26.9 (0.67) during the awake state, decreased to 17.1 (1.2) during the anaesthetized state, and increased again to 26.1 (0.93) when the patients regained full consciousness.

CONCLUSION

PPF is the best of the four studied MLAEP parameters for the clinical characterization of the anaesthetized state during surgery.

Desflurane induces more cerebral vasodilation than isoflurane at the same A-line autoregressive index level

BACKGROUND

Clinical use of desflurane in neuroanesthesia remains under debate. Comparison of dose-dependent vasodilatory properties between desflurane and isoflurane, the more traditional volatile agent for clinical neuroanesthesia, requires equianesthetic dosing of the agents. Reproducible neurophysiological measurements of the level of anesthesia in an individual, e.g. the A-line autoregressive index (AAI), can be used for an equipotent dosage of two volatile agents in the same individual.

METHODS

Desflurane and isoflurane, in randomized order, were titrated to a stable AAI level of 15-20 in 18 ASA I or II patients. The mean flow velocity (Vmca) and pulsatility index (PI) in the middle cerebral artery were then measured with transcranial Doppler at an end-tidal CO(2) concentration of 4.4%.

RESULTS

For desflurane Vmca was 11% higher [95% confidence interval (CI), 5-18%; P = 0.0020] and PI was 13% lower (95% CI, 3-23%; P = 0.0083) than for isoflurane. The mean arterial blood pressure did not differ between the agents. The fraction of MAC necessary for the intended AAI level was 35% lower (95% CI, 20-49%; P = 0.00016) with desflurane than with isoflurane.

CONCLUSION

Desflurane was associated with more cerebral vasodilation than isoflurane at the same depth of anesthesia, as indicated by the AAI. This attributes further reason for caution in the use of desflurane in clinical neuroanesthesia. The difference between desflurane and isoflurane in the MAC fractions required for the same AAI level confirms the limitations of MAC in defining the level of anesthesia.

Peroperative depth of anaesthesia may influence postoperative opioid requirements

BACKGROUND

Studies on monitoring the depth of anaesthesia have shown that with the use of these monitors the peroperative consumption of anaesthetics can be reduced. Studies have also indicated that the peroperative depth of anaesthesia may affect the postoperative course. The purpose of this study was to evaluate a possible relation between the depths of anaesthesia and the postoperative pain score and consumption of morphine.

METHODS

We used middle latency auditory evoked potentials (MLAEPs) for monitoring the depth of anaesthesia. The study was prospective, observer blinded and included 50 women scheduled for elective abdominal hysterectomy. Anaesthesia was induced using propofol and remifentanil. Before leaving the recovery room the patients were provided with a programmed patient-controlled pump (PCA), which was only activated on demand. Auditory evoked potentials were registered from just before induction of anaesthesia and during the whole procedure, but the anaesthetist did not have access to the monitor.

RESULTS

Fifty patients were included and seven were excluded. The remaining 43 patients were divided into two groups: Group High (n=12) with an AAI>28 for >5% of the registration time and Group Low (n=31) with an AAI>28 for <5% of the registration time. Group High had significantly higher morphine requirements in the recovery and activated the PCA-pump more frequently during the first 24 postoperative hours.

CONCLUSION

The results indicate that the peroperative depth of anaesthesia may have effects on the postoperative analgesic requirements.

Sevoflurane induces less cerebral vasodilation than isoflurane at the same A-line autoregressive index level

BACKGROUND

The use of sevoflurane in neuroanesthesia is still under debate. Comparison of dose-dependent vasodilatory properties between sevoflurane and isoflurane, the more traditional neuroanesthetic agent, requires comparable dosing of the agents. A-line autoregressive index (AAI) provides reproducible individual measurement of anesthetic depth.

METHODS

Sevoflurane and isoflurane, in randomized order, were titrated to a stable AAI of 15-20 in each of 18 ASA I or II patients. The mean flow velocity (Vmca) and pulsatility index (PI) in the middle cerebral artery were measured with transcranial Doppler at an end-tidal CO2 of 4.5%.

RESULTS

For sevoflurane Vmca was 18% lower [95% confidence interval (CI) 12-22%; P < 0.00001] and PI was 23% higher (95% CI 12-33%; P = 0.0013) than for isoflurane. Mean arterial blood pressure did not differ between the two agents. The minimum alveolar concentration (MAC) fraction necessary to reach the intended AAI level was 13% higher (95% CI 5-20%; P = 0.0079) with sevoflurane than with isoflurane.

CONCLUSION

Sevoflurane induced less cerebral vasodilation than isoflurane at the same depth of anesthesia, measured by AAI, and hence seems more favorable for clinical neuroanesthesia. In our opinion the difference between sevoflurane and isoflurane in the MAC fraction required to attain the same AAI level demonstrates the limitations of MAC in defining the level of anesthesia.

ARX-derived auditory evoked potential index and bispectral index during the induction of anesthesia with propofol and remifentanil

A new commercial auditory evoked potential (AEP) monitor (A-line AEP monitor) was developed to calculate an index (ARX AEP index; AAI) by automatically using the amplitudes and latencies of the AEP. We investigated 30 patients before spine surgery. AAI; bispectral index (BIS); relative (%) delta, theta, alpha, and beta; spectral edge frequency; median frequency; mean arterial blood pressure; heart rate; and oxygen saturation were obtained simultaneously during stepwise (1.0 micro g/mL) induction of target-controlled propofol concentration until 5.0 micro g/mL, followed by an infusion of 0.3 micro g. kg(-1). min(-1) of remifentanil. Every minute, the patients were asked to squeeze the observer's hand. Prediction probability (Pk), receiver operating characteristic, and logistic regression were used to calculate the probability to predict the conditions AWAKE, UNCONSCIOUSNESS (first loss of hand squeeze), and steady-state ANESTHESIA (5.0 micro g/mL of propofol and 0.3 micro g. kg(-1). min(-1) of remifentanil). Although a statistically significant difference among the conditions was observed for AAI, BIS, mean arterial blood pressure, median frequency, and %alpha, only AAI and BIS were able to distinguish UNCONSCIOUSNESS versus AWAKE and ANESTHESIA versus AWAKE with better than Pk = 0.90. The modern electroencephalographic variables AAI and BIS were superior to the classic electroencephalographic and hemodynamic variables to distinguish the observed anesthetic conditions.

IMPLICATIONS

The modern electroencephalographic ARX-derived auditory evoked potential index and the bispectral index were superior to the classic electroencephalographic and hemodynamic variables for predicting anesthetic conditions. Variables derived from the auditory evoked potential did not provide an advantage over variables derived from spontaneous electroencephalogram.

Fuzzy pharmacology: theory and applications

Fuzzy pharmacology is a term coined to represent the application of fuzzy logic and fuzzy set theory to pharmacological problems. Fuzzy logic is the science of reasoning, thinking and inference that recognizes and uses the real world phenomenon that everything is a matter of degree. It is an extension of binary logic that is able to deal with complex systems because it does not require crisp definitions and distinctions for the system components. In pharmacology, fuzzy modeling has been used for the mechanical control of drug delivery in surgical settings, and work has begun evaluating its use in other pharmacokinetic and pharmacodynamic applications. Fuzzy pharmacology is an emerging field that, based on these initial explorations, warrants further investigation.

Changes in the rapidly extracted auditory evoked potentials index and the bispectral index during sedation induced by propofol or midazolam under epidural block

BACKGROUND

The bispectral index (BIS) and the rapidly extracted auditory evoked potentials index (A-line ARX Index or AAI) have been proposed as methods to measure the depth of sedation. A prospective study was designed to assess the performance of both these methods for measuring the depth of sedation induced by propofol or midazolam under epidural block.

METHODS

Thirty-two ASA I and II adult patients undergoing elective gynaecological surgery under low-thoracolumbar epidural block were studied. Eighteen patients received propofol (Group P: 20 mg bolus every 3 min) and 14 received midazolam (Group M: 0.5 mg bolus every 5 min) until an observer's assessment of alertness/sedation (OAA/S) scale score of 1 was achieved. AAI and BIS were monitored for different OAA/S scores.

RESULTS

AAI and BIS decreased and increased following the changes on the patients' OAA/S scores and correlated with sedation significantly. During the onset phase, the coefficients of Spearman's rank correlation for AAI and BIS were respectively 0.958 and 0.898 (P < 0.001) for Group P, and 0.973 and 0.945 (P < 0.001) for Group M. During the recovery phase in Group P, the coefficients were respectively 0.946 and 0.702 (P < 0.001). Linear regression analysis showed that both AAI and BIS were linearly related to the OAA/S scores. The coefficients of Spearman's rank correlation and linear regression for AAI were all greater than those for BIS (P < 0.05).

CONCLUSIONS

Both AAI and BIS correlated well with the depth of sedation induced by propofol or midazolam under epidural block. AAI may be more valuable when monitoring depth of sedation.

Comparison of auditory evoked potentials and the A-line ARX Index for monitoring the hypnotic level during sevoflurane and propofol induction

BACKGROUND

Extraction of the middle latency auditory evoked potentials (AEP) by an auto regressive model with exogenous input (ARX) enables extraction of the AEP within 1.7 s. In this way, the depth of hypnosis can be monitored at almost real-time. However, the identification and the interpretation of the appropriate signals of the AEP could be difficult to perform during the anesthesia procedure. This problem was addressed by defining an index which reflected the peak amplitudes and latencies of the AEP, developed to improve the clinical interpretation of the AEP. This index was defined as the A-line Arx Index (AAI).

METHODS

The AEP and AAI were compared with the Modified Observers Assessment of Alertness and Sedation Scale (MOAAS) in 24 patients scheduled for cardiac surgery, anesthetized with propofol or sevoflurane.

RESULTS

When comparing the AEP peak latencies and amplitudes and the AAI, measured at MOAAS level 5 and level 1, significant differences were achieved. (mean(SD) Nb latency: MOAAS 5 51.1 (7.3) ms vs. MOAAS 1: 68.6 (8.1) ms; AAI: MOAAS 5 74.9 (13.3) vs. MOAAS 1 20.7 (4.7)). Among the recorded parameters, the AAI was the best predictor of the awake/anesthetized states.

CONCLUSION

We conclude that both the AAI values and the AEP peak latencies and amplitudes correlated well with the MOAAS levels 5 (awake) and 1 (anesthetized).

Simulation in anesthesia: the merits of large simulators versus small simulators

Anesthesia simulation is generally perceived as involving large simulators that provide a limited number of operating room scenarios, especially crisis management. The scope of both anesthesia and flight simulation is much wider, and this review summarizes the range of the former. The areas where simulation has been used include training, education and science. The diversity of its uses may surprise the reader. The models that are used in simulations are important, and these are discussed in part of the discussion. As a result of the current imbalance in perception, I emphasize the merits of small simulators at the expense of large simulators.

Changes in rapidly extracted auditory evoked potentials during tracheal intubation

BACKGROUND

One of the problems encountered in assessment of the hypnotic level during anesthesia is the extraction of a consistent and reliable measure online and close to real time. Hemodynamic parameters such as heart rate and blood pressure are not, at least with the traditional single parameter versus time presentation, adequate for ensuring an optimal level of anesthesia, especially when using neuromuscular blocking agents (NMBA). In the literature, it has been demonstrated that auditory evoked potentials (AEP) are able to provide two aspects relevant to determining level of anesthesia: firstly, they have identifiable anatomical significance and, secondly, their characteristics reflect the way the brain perceives a stimulus.

METHODS

The aim of this study was to evaluate the AEP index based on a system identification model, the autoregressive model with exogenous input (ARX-model), and to compare it to the classical method, the moving time average (MTA). The ARX enables the extraction within 15-25 sweeps, depending on the signal-to-noise ratio (SNR), whereas MTA typically needs 250-500 sweeps. The hypothesis of the present study was that since the ARX-model extracts the AEP faster than the MTA-model, the former should be able to detect changes during the brief, intense stimulus of endotracheal intubation. Twelve female patients scheduled for gynecological surgery were included in the study. Anesthesia was initiated with thiopentone and maintained with isoflurane and alfentanil. The AEP was mapped into an index (AEP-index) normalized to 100 when the individual was awake and decreasing to an average of 25 during thiopentone induced anaesthesia. The results were compared to those obtained by MTA-extracted AEP.

RESULTS

During tracheal intubation 9 patients showed an increase in the ARX-extracted AEP-index larger than 15, and 6 of these patients showed an increase larger than 25 (mean increase=33, SD=18). The MTA-extracted AEP-index showed only one patient with an increase larger than 15. The ARX-extracted AEP changed significantly faster than the MTA-extracted AEP.

CONCLUSION

The ARX-extracted AEP-index increases during tracheal intubation. There is a significant difference between the ARX-extracted AEP and the traditional MTA-extracted AEP, in terms of response time. In order to trace short-lasting changes in the hypnotic level by AEP, the AEP should be extracted by a method with a fast response such as the ARX-model.