A system to detect gradual change in long term EEG /

Abstract

A system was developed for detecting gradual changes in background EEG, with a view to simplifying the tedious task of long-term monitoring in the neurological intensive care unit.Six-channel EEG recordings serve as input to the system. EEG from each hemisphere is segmented into variable length epochs described by nine-dimensional feature vectors. A fixed scaling operation is performed so that differentiation between segments using the Euclidean distance between feature vectors relies equally on each feature.Cluster analysis is performed to select five types of EEG that best represent the data spread for each hour. Color-coded circles representing clusters from every hour are mapped on a two-dimensional plot. The relative location of these circles reflects the difference between their associated EEG. Change between hours is detected by observing the movement of circles associated with each hour.Detection of change with this method agreed closely with that observed via manual review but was much simpler