Approximate Entropy (ApEn) and Permutation Entropy (PE) have been recently introduced for assessment of anesthetic depth. Both measures have previously been shown to track changes in the electrical brain activity related to the administration of anesthetic agents. In this paper ApEn and PE are compared for the automatic classification of ‘awake’ and ‘anesthetized’ state using a Support Vector Machine to assess their robustness for potential use in a device for monitoring awareness during general anesthesia. It was found that both measures provide linearly separable features and we are able to discriminate between the two states with accuracy greater than 96% using either of the two entropy measures.
