EEG Signal Processing in Anesthesia Feature Extraction of Time and Frequency Parameters

Electroenccephalogram (EEG) is well established for assessing the functional state of the brain. During the anesthesia, the brain activity level changed dramatically, so we could get the depth of anesthesia estimation from EEG recording. In previous research, most achievements were in the frequency-domain. In this paper, besides the frequency-domain features extraction, we have introduced the concept of “Entropy” as well as nonlinear feature. The results show that, with the deepening of anesthesia degree, approximate entropy (ApEn), Shannon entropy (SSE) and Lempel— Ziv complexity from EEG signal decrease gradually. Centre frequency towards to low frequency, total power shows a rising trend. Largest Lyapunov index also decreases, while the correlation dimension has no clear trend.