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.
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