PREDICTION OF RESPONSE TO INCISION USING THE MUTUAL INFORMATION AND COMPLEXITY OF ELECTROENCEPHALOGRAMS DURING ANAESTHESIA

The need for a reliable method of estimating depth of anaesthesia has existed since the introduction of anesthesia. This paper presents a new approach to predict response during isoflurane anaesthesia by using mutual infomation time series of electroencephalograms and their complexity analysis. The mutual infomation time series between four leads were first computed using the EEG time series. The Lempel Ziv complexity measures, C(n)s, were extracted from the mutual infomation time series by complexity analysis. Prediction was made by means of neural network(ANN). The input to the neural network was the C(n) values and the MAC level, the output was results of the prediction. From 98 consenting patient experiments, 98 distinct EEG recordings were collected prior to incision during isoflurane anaesthesia of different levels. Hemodynamic variables and respiration pattern were also monitored. After skin incision, each patient was observed carefully for 2 min to detect purposeful responses and then the EEG was labelled as 0.0 for responder or as 1.0 for non-responder. Training and testing the ANN used the drop one person method, response prediction was tested by monitoring the response to incision and the result given by the ANN. The system was able to correctly classify subsequent response with an average accuracy of 91.84%. The results showed that the method has a better performance than others, such as spectral edge frequency, median frequency, and bispectral analysis. This method is computationally fast and acceptable real time clinical performance could be obtained.