[Approximate entropy of anesthesia EEG: compromise between calculation time and clinical validity]. / Approximate Entropy von Narkose-EEG: Kompromiss zwischen Rechenzeit und klinischer Aussagekraft.

Approximate entropy, a measure of regularity, can be used to analyze the electroencephalogram of patients in general anesthesia to discriminate between different states of consciousness. EEG burst suppression patterns reflect a state of deep anesthesia. Due to the instationary character of this EEG pattern approximate entropy values do not correctly classify the patient state. Possible solutions to this problem may be limited by the demand of computing power for entropy calculation and the reaction time following changes in patient state. Different approaches for an online monitoring application are examined.

Concurrent recording of AEP, SSEP and EEG parameters during anaesthesia: a factor analysis.

BACKGROUND: Spontaneous EEG, mid-latency auditory evoked potentials (AEP) and somatosensory evoked potentials (SSEP) have been used to monitor anaesthesia. This poses the question as to whether or not EEG, AEP and SSEP vary in parallel with varying conditions during surgical anaesthesia. METHODS: A total of 81 variables (31 EEG, 22 SSEP, 28 AEP) were simultaneously recorded in 48 surgical patients during anaesthesia. A total of 307 cases of the 81 variables in stable anaesthetic states were recorded. A factor analysis was performed for this data set. RESULTS: Sixteen variables were excluded because of multicollinearity. We extracted 13 factors with eigenvalues >1, representing 78.3% of the total variance, from the remaining 65 x 307 matrix. The first three factors represented 12%, 11% and 10% of the total variance. Factor 1 had only significant loadings from EEG variables, factor 2 only significant loadings from AEP variables and factor 3 only significant loadings from SSEP variables. CONCLUSION: EEG, AEP and SSEP measure different aspects of neural processing during anaesthesia. This gives rise to the hypothesis that simultaneous monitoring of these quantities may give additional information compared with the monitoring of each quantity alone.

Awareness and the EEG power spectrum: analysis of frequencies.

BACKGROUND: Power spectral analysis is a well-established method for the analysis of EEG signals. Spectral parameters can be used to quantify pharmacological effects of anaesthetics on the brain and the level of sedation. This method, in numerous variations, has been applied to depth of anaesthesia monitoring and has been incorporated into several commercially available EEG monitors. Because of the importance of EEG spectral analysis, we evaluated the performance of each frequency in the power spectrum regarding detection of awareness. METHODS: Ninety artefact-free EEG segments of length 8 s were obtained from a database that contains perioperatively recorded EEG data. For the present analysis, EEG data were selected from 39 patients with propofol-remifentanil or sevoflurane-remifentanil anaesthesia with a period of awareness. Half of the EEG segments were recorded during periods of awareness as defined by an adequate response to the command 'squeeze my hand'. The other half were from unresponsive patients. The power spectral density was calculated for each segment. The performance of each frequency bin of the power spectrum as a detector of awareness was assessed with a remapped prediction probability rPK, i.e. the prediction probability PK mapped to a range of 0.5-1. RESULTS: The remapped prediction probability was high (rPK>0.8) for low frequencies (<15 Hz) and for high frequencies (>26 Hz), with a minimum (rPK<0.55) at 21 Hz. Indentations in the 'performance spectrum' occur at the power-line frequency (50 Hz) and its harmonics and at 78 Hz, probably caused by the continuous impedance measurement of another device used in parallel. With the exception of the indentations, the remapped prediction probability of the high frequencies (>35 Hz) was >0.95. CONCLUSIONS: The best performance for the detection of awareness was achieved by EEG power spectral frequencies from >35 Hz up to 127 Hz. This frequency band may be dominated by muscle activity. The frequency band between 15 and 26 Hz may be of limited value, as reflected by lower rPK values.

Wavelet analysis of middle latency auditory evoked responses: calculation of an index for detection of awareness during propofol administration.

BACKGROUND: Middle latency auditory evoked responses (MLAER) as a measure of depth of sedation are critically dependent on data quality and the analysis technique used. Manual peak labeling is subject to observer bias. This study investigated whether a user-independent index based on wavelet transform can be derived to discriminate between awake and unresponsive states during propofol sedation. METHODS: After obtaining ethics committee approval and written informed consent, 13 volunteers and 40 patients were studied. In all subjects, propofol was titrated to loss of response to verbal command. The volunteers were allowed to recover, then propofol was titrated again to the same end point, and subjects were finally allowed to recover. From three MLAER waveforms at each stage, latencies and amplitudes of peaks Pa and Nb were measured manually. In addition, wavelet transform for analysis of MLAER was applied. Wavelet transform gives both frequency and time information by calculation of coefficients related to different frequency contents of the signal. Three coefficients of the so-called wavelet detail level 4 were transformed into a single index (Db3d4) using logistic regression analysis, which was also used for calculation of indices for Pa, Nb, and Pa/Nb latencies. Prediction probabilities for discrimination between awake and unresponsive states were calculated for all MLAER indices. RESULTS: During propofol infusion, subjects were unresponsive, and MLAER components were significantly depressed when compared with the awake states (P < 0.001). The wavelet index Db3d4 was positive for awake and negative for unresponsive subjects with a prediction probability of 0.92. CONCLUSION: These data show that automated wavelet analysis may be used to differentiate between awake and unresponsive states. The threshold value for the wavelet index allows easy recognition of awake versus unresponsive subjects. In addition, it is independent of subjective peak identification and offers the advantage of easy implementation into monitoring devices.

Concept for an intelligent anaesthesia EEG monitor.

Considering the fundamental difficulties to define the term 'depth of anaesthesia', a more feasible concept for assessment of 'adequacy of anaesthesia' will be explained. The basic requirements for a monitoring index are definite response, gradual scaling and independence from the anaesthetic technique used. Additionally the index should be predictive for appearance of clinical signs of an inadequate anaesthesia. Different signal-processing methods will be discussed to extract the relevant information from both the spontaneous and the evoked brain electrical activity. In this context well established methods like spectral analysis are investigated in combination with new and more sophisticated methods like bispectral analysis or wavelet decomposition. Since no single-parameter index has been defined for monitoring depth of anaesthesia, a set of EEG parameters may be more useful to take into account intra- and interindividual variability. In parallel to the description of the monitor concept, the investigation of neural nets and fuzzy techniques, in addition to or in substitution of conventional statistical methods, will be introduced. Examples are given for data quality assessment, parameter extraction and re-classification.

Middle latency auditory evoked responses and electroencephalographic derived variables do not predict movement to noxious stimulation during 1 minimum alveolar anesthetic concentration isoflurane/nitrous oxide anesthesia.

UNLABELLED: The electroencephalogram (EEG) and middle latency auditory evoked responses (MLAER) have been proposed for assessment of the depth of anesthesia. However, a reliable monitor of the adequacy of anesthesia has not yet been defined. In a multicenter study, we tested whether changes in the EEG and MLAER after a tetanic stimulus applied to the wrist could be used to predict subsequent movement in response to skin incision in patients anesthetized with 1 minimum alveolar anesthetic concentration (MAC) isoflurane in N2O. We also investigated whether the absolute values of any of these variables before skin incision was able to predict subsequent movement. After the induction of anesthesia with propofol and facilitation of tracheal intubation with succinylcholine, 82 patients received 1 MAC isoflurane (0.6%) in N2O 50% without an opioid or muscle relaxant. Spontaneous EEG and MLAER to auditory click-stimulation were recorded from a single frontoparietal electrode pair. MLAER were severely depressed at 1 MAC isoflurane. At least 20 min before skin incision, a 5-s tetanic stimulus was applied at the wrist, and the changes in EEG and MLAER were recorded. EEG and MLAER values were evaluated before and after skin incision for patients who did not move in response to tetanic stimulation. Twenty patients (24%) moved after tetanic stimulation. The changes in the EEG or MLAER variables were unable to predict which patients would move in response to skin incision. Preincision values were not different between patients who did and did not move in response to skin incision for any of the variables. MLAER amplitude increased after skin incision. We conclude that it is unlikely that linear EEG measures or MLAER variables can be of practical use in titrating isoflurane anesthesia to prevent movement in response to noxious stimulation. IMPLICATIONS: Reliable estimation of anesthetic adequacy remains a challenge. Changes in spontaneous or auditory evoked brain activity after a brief electrical stimulus at the wrist could not be used to predict whether anesthetized patients would subsequently move at the time of surgical incision.