Designing a Fuzzy Rule Based System to Estimate Depth of Anesthesia

Estimating the depth of anesthesia (DOA) is still a challenging area in anesthesia research. The objective of this study was to design a fuzzy rule based system which integrates electroencephalogram (EEG) features to quantitatively estimate the DOA. The proposed method is based on the analysis of single-channel EEG using frequency and time domain features as well as Shannon entropy measure. The fuzzy classifier is trained with features obtained from four subsets of data comprising welldefined anesthesia states: awake, moderate, general anesthesia, and isoelectric. The classifier extracts efficient fuzzy if-then rules and the DOA index is derived between 100 (full awake) to 0 (isoelectric) using fuzzy inference engine. To validate the proposed method, a clinical study has conducted on 22 patients to construct 4 subsets of reference states and also to compare the results with CSM monitor (Danmeter, Denmark), which has revealed satisfactory correlation with clinical assessments.

• Type: Conference
• Year: 2007
• Where: CIDM
• Authors: V. Esmaeili, Amin Assareh, M. B. Shamsollahi, Mohammad Hassan Moradi, N. M. Arefian
• Keywords: Anesthesia, Artificial Intelligence, CIDM 2007, Fuzzy Classifier, Welldefined Anesthesia States