Abstract

The rationale for automatically monitoring anesthetic drug effects on the central nervous system (CNS) is to improve possibilities to gain objective information on a patient's state and to adjust the medication individually. Although monitors have shown their usefulness in practice, there are still a number of unclear issues, especially with respect to the scientific foundations and validity of CNS monitoring techniques, and in monitoring the light hypnotic levels. Current monitors are, for example, often based on heuristics and ad hoc solutions. However, a quantitative index for anesthetic drug effect should have a sound relationship with observations and with the selected control variable. The research objectives are: (1) to explore propofol anesthetic related neurophysiological phenomena that can be applied in the automatic assessment of CNS suppression; (2) to develop a valid control variable for this purpose; (3) by means of digital signal processing and mathematical modeling, to design and to evaluate the performance of an index that correlates with the control variable.

This dissertation introduces potentially useful neurophysiological phenomena, such as changes in phase synchronization between different EEG channels due to anesthesia, and painful stimulus evoked responses during the burst suppression. Furthermore, it refines the progression of the time-frequency patterns during the induction of anesthesia and shows their relation to the instant of unresponsiveness. The presented spontaneous and evoked EEG phenomena provide complementary information about the CNS functional suppression.

Most significantly, the dissertation proposes a continuous and observation based control variable (r scale) and the means to predict its values by using EEG data. The definition of the scale provides a basis for anticipating the instant of the loss of consciousness. Additionally, the phase synchronization index as an indicator of drug effect is introduced. The approximate entropy descriptor performance is evaluated and optimised with a non-stationary signal recorded during the induction of anesthesia.

The results open up opportunities to improve the preciseness, scientific validity and the interpretation of information on the anesthetic effects on CNS, and therefore, to increase the reliability of the anesthesia monitoring. Further work is needed to extend and verify the results in deep anesthesia.