Neurophysiological Monitoring - the Clinician

Abstract

Electroencephalogram (EEG) and auditory evoked potentials (AEP) have been suggested to monitor the level of consciousness during anesthesia, in order to complete the information given by vital signs during surgery and to reduce risk of intraoperative awareness [1,2]. Appropriate EEG- and AEP-parameters should calculate numerical values from the signals which are related to the hypnotic component of anesthesia. Therefore, EEG-and AEP-data of several studies approved by the ethics committee of the faculty of medicine, Technische Universität München, were used to develop and evaluate parameters of EEG/AEP such as multi-parametric indicators of “depth of anesthesia” [3-5].

Development with respect to the separation of consciousness from unconsciousness is based on data of a clinical study involving 40 patients undergoing general anesthesia [3]. 15 volunteers were enrolled into a further study which was designed to find an indicator reflecting the entire range from consciousness to deep anesthesia [5]. In order to verify the performance of parameters and indicators, a multicenter study in 6 European centers was performed [4]. The study was designed to evaluate, whether parameters and indicators separate consciousness from unconsciousness and indicate increase and decrease of the anesthetic level in the entire range from light sedation to deep anesthesia. Written informed consent was obtained from 263 adult patients undergoing surgery under general anesthesia and each patient was assigned to one of 11 anesthetic combinations, consisting of different opioid analgesics and hypnotic drugs. Monitoring included standard parameters, EEG and AEP. Data were recorded and stored together with standardized clinical comments allowing offline analysis of parameters and indicators based on NeuMonD platform [6]. Different EEG- (e.g. linear spectral, nonlinear entropy based) and AEP-parameters (wavelet based) were included in analysis and combined to indicators within the framework of multivariate logistic regression, fuzzy inference, rough set theory and support vector machines [7-10]. The investigations confirm promising result of developed “depth of anesthesia” indicators using NeuMonD platform. In contrast to monitors such as BIS (Aspect Medical Systems Inc., USA) the presented indicators are based on the classical EEG band (< 30Hz) in order to avoid dependence of muscle activity (EMG) for detection of consciousness. This may be beneficial because the inclusion of high frequencies may provoke that not only the activity of cortical neurons is reflected, but also EMG which compromise the detection of potential awareness.