Abstract / truncated to 115 words (read the full abstract)

In this thesis, it was aimed to propose a new parameter for estimation of depth of anaesthesia by using 15 channel EEG. The recordings were taken from 30 subjects undergoing general anaesthesia for gynecological surgery. The offline processing was realized in MATLAB. First part of the thesis involved literature search of analysis methods that are currently used in current commercial depth of anaethesia monitors and simulations were done. As a result of spectral analysis of EEG channels, the application of connectivity was proposed between channels that were also shown to be active under anaesthesia. By using Multivariate Autoregressive Modeling and Timevarying Partial Directed Coherence values were extracted and the evolution of connectivity changes during deepening ... toggle 5 keywords

EEG – depth of anaesthesia – granger causality – multivariate ar modeling – partial directed coherence

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