Abstract
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 methods. A clinical study was conducted on 22 patients to construct subsets of reference data corresponding to four well-defined anesthetic states: awake, moderate anesthesia, surgical anesthesia and isoelectric.
Statistical analysis of features was used to design input membership functions (MFs). The input space was partitioned with respect to the derived MFs and the training data was used to label the partitions and extract efficient fuzzy if-then rules. Consequently, the fuzzy rule-base index (FRI) is derived between 0 (isoelectric) to 100 (fully awake) using fuzzy inference engine and designed output MFs.
We also applied the same features to an adaptive network-based fuzzy inference system (ANFIS) derived without any prior knowledge. The results show that FRI correlates more with the clinically accepted DOA index, CSI™ (CSM, Danmeter, Denmark). In addition to this achievement the main idea behind this study is to simplify the mutual knowledge exchange between the human expert and the machine, leading to enhance both interpretability of the results and performance of the system.