Adaptive segmentation of burst-suppression pattern in isoflurane and enflurane anesthesia

In this paper a developed novel algorithm for adaptive segmentation of Burst-suppression EEG is presented. The algorithm can detect bursts, suppression and artifacts, dividing the signal into corresponding segments. A compact representation of burst-suppression EEG, useful in monitoring long-term recordings, is presented. In the second part of the paper the burst-suppression patterns of isoflurane and enflurane anesthesia are compared. It is found that bursts as well as suppression segments are shorter in enflurane anesthesia while the coefficient of variability of the segment lengths is similar for the two anesthetics.