Consciousness and depth of anesthesia assessment based on Bayesian analysis of EEG signals

This study applies Bayesian techniques to analyze EEG signals for the assessment of the consciousness and depth of anesthesia (DoA). This method takes the limiting large-sample normal distribution as posterior inferences to implement the Bayesian paradigm. The maximum a posterior (MAP) is applied to denoise the wavelet coefficients based on a shrinkage function. When the anesthesia states change from awake to light, moderate, and deep anesthesia, the MAP values increase gradually. Based on these changes, a new function B(DoA) is designed to assess the DoA. The new proposed method is evaluated using anesthetized EEG recordings and BIS data from 25 patients. The Bland-Alman plot is used to verify the agreement of B(DoA) and the popular BIS index. A correlation between B(DoA) and BIS was measured using prediction probability P(K). In order to estimate the accuracy of DoA, the effect of sample n and variance τ on the maximum posterior probability is studied. The results show that the new index accurately estimates the patient's hypnotic states. Compared with the BIS index in some cases, the B(DoA) index can estimate the patient's hypnotic state in the case of poor signal quality.