A data-reduction process for long-term EEGs. Feature extraction through digital processing in a multiresolution framework

Describes a contribution to a data-reduction process to be used with long-term EEGs. Since typical long-term EECs recorded from depth electrodes are extended over several days, while epilepsy may be characterized by occasional transients, data reduction is an important consideration for the electroencephalographer. The electroencephalographer detects epileptic activity by visual inspection of the EEG, which is a time-consuming procedure for records that are days long. The result obtained with the authors´ proposed algorithm is the selection of segments of EEG where a transient is detected; then these segments are reviewed by an expert. The authors´ primary objective is to minimize the visual inspection process, presenting to the clinician only selected segments of the EEG. Throughout this article, no distinction is made among the wide variety of epileptiform transients. The only objective of the algorithm is to separate background activity from epileptiform activity.