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Estimation of depth of anesthesia using the midlatency auditory evoked potentials by means of neural network based multiple classifier system

This paper proposes a neural network based multiple classifier system (MCS) for monitoring the depth of anesthesia by assessing the characteristics of the middle latency auditory potentials (MLAEP) and the Propofol effect-site concentration. The system is composed of three individual neural network based classifiers with different sets of features. Discrete wavelet transformation (DTWT) and power spectrum estimation (PSD) were utilized to extract the MLAEP features. A Bayesian combination rule was then applied to evaluate the final decision by combining the results of the three individual classifiers. From total of 113 data samples only one was incorrectly classified and the misclassified sample belonged to a positive response. The system achieved a 99% accuracy rate for classifying anesthesia depth.