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Monitoring the anesthetic-induced unconsciousness (hypnosis) using wavelet analysis of the electroencephalogram Zikov, Tatjana

Abstract

This thesis presents a novel method for the discrimination between various levels of anesthetic-induced unconsciousness (i.e. hypnosis) in the context of intra-operative anesthesia. This is achieved by means of the wavelet analysis of the patient's Electroencephalogram (EEG). While monitors of hypnosis are already available, they rely on complex signal processing algorithms, and in some instances, on multivariate statistical analysis of a large quantity of data. To date, the BIS® monitor (Aspect Medical Systems, Inc., MA) provides the most accepted solution. It displays an index calculated based on bispectral analysis, and has been the subject of many clinical trials over the past 5 years. As such, it is widely known and accepted by the medical community, particularly in the United States. Wavelet analysis can be viewed as a generalization of Fourier analysis that introduces time localization in addition to frequency decomposition of a signal. This major advantage of wavelets makes them particularly suitable for the analysis of non-stationary signals such as the EEG. In this thesis, we present a simple wavelet-based technique that calculates, in real time, an index of hypnosis (so-called WAV Index) based on a patient's electroencephalogram. Wavelet analysis significantly reduces the computational complexity to perform this task. Further, neither a large number of patients nor an extensive amount of clinical EEG data is needed for the index derivation. In addition, a technique for the de-noising of the electroencephalogram to remove ocular artifacts is proposed. These artifacts, which result from ocular activity (eye blinks and movements), strongly perturb the EEG signal by superimposing large spikes and step-like patterns. Hypnotic states such as the awake state and light sedation are typically corrupted by these waveforms. The solution proposed successfully removes these artifacts from the EEG, thus providing a clean signal for the wavelet analysis. This technique further works in the case of head movement artifacts. Finally, clinical validation has been carried out to assess the performance of the proposed index. While being particularly well-correlated to the bispectral index, the proposed index has a faster response to fast transients and it is more consistent in terms of patient intra-variability.

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