A TREND-DETECTION ALGORITHM FOR INTRAOPERATIVE EEG MONITORING

Intraoperative EEG-monitoring needs to discriminate random fluctuation s from real systematic variations (trends). This task is made more dif ficult by several types of artifacts. With the goal of supporting visu al EEG evaluation, a new trend-detection algorithm is presented which is based on spectral analysis and a post-processing dynamic linear mod el, the latter introduced by Harrison and Stevens(1). A gradient value provided by this model is exploited to determine the onset and relati ve extent of an existing trend. Artifacts are detected by several thre shold measures for the original signal and its first derivative. The s ystem was validated using a set of intraoperative EEGs recorded during carotid endarterectomy. Copyright (C) 1996 Elsevier Science for IPEMB .