• E Kochs, G Stockmanns, C Thornton, W Nahm, and C J Kalkman.
• Klinik für Anaesthesiologie, Technischen Universität München, Klinikum rechts der Isar, Germany. E.F.Kochs@lrz.tum.de
• Anesthesiology. 2001 Nov 1; 95 (5): 1141-50.

BackgroundMiddle 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.MethodsAfter 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.ResultsDuring 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.ConclusionThese 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.

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