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.
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.
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.
Book Chapters (1)
J. Petersen, G. Stockmanns, and W. Nahm. EEG Analysis for Assessment of Depth of Anaesthesia. In Fuzzy Systems in Medicine, P. Szczepaniak, P. Lisboa, J. Kacprzyk (eds), Physica-Verlag, Heidelberg, pp. , 2000
Up to now one unsolved challenge in anaesthesia is the assessment of depth of anaesthesia during surgery. No general purpose on-line monitoring system predicting depth or quality of anaesthesia exists. The analysis of spontaneous (EEG) and evoked electrical brain activities (AEP) leads to methods assessing depth of anaesthesia. A monitor concept was developed consisting of the three functional components EEG recorder, pre-processor and knowledge based discriminator including an inductive learning algorithm generating fuzzy decision trees. By their statistical evaluation feature vectors for training Kohonen networks are selected aplied for re-classification tests of clinical study data.
Conference Contributions (12)
G. Stockmanns, J. Abke, and W. Nahm. Extraktion relevanter Parameter aus wavelet-transformierten akustisch evozierten Potentialen zur Bestimmung inadäquater Anästhesie mit Hilfe des Kohonen-Netzes. In Biomedizinische Technik / Biomedical Engineering, vol. 41(s1) , pp. 234-235, 1996
G. Stockmanns, E. Kochs, W. Nahm, and M. Brunner. Automatic analysis of auditory evoked potentials by means of wavelet analysis. In Memory and Awareness in Anaesthesia IV, Proc. 4th International Symposium, pp. 117-131, 2000
G. Stockmanns, W. Nahm, J. Petersen, C. Thornton, and H. D. Kochs. Wavelet-Analyse akustisch evozierter Potentiale während wiederholter Propofol-Sedierung - Wavelet analysis of acoustically evoked potentials during repeated propofol sedation. In Biomedizinische Technik. Biomedical Engineering, vol. 42 Suppl, pp. 373-374, 1997
W. Nahm, G. Stockmanns, M. Daumer, J. Abke, and E. Konecny. Automatische EEG-Datenvorverarbeitung in einer Multicenterstudie - Automatic EEG data processing in a multicenter study. In Biomedizinische Technik/Biomedical Engineering, vol. 43(s1) , pp. 146-147, 1998