TIME-FREQUENCY ANALYSIS USING THE MATCHING PURSUIT ALGORITHM APPLIED TO SEIZURES ORIGINATING FROM THE MESIAL TEMPORAL-LOBE

Objectives: The ability to analyze patterns of recorded seizure activi ty is important in the localization and classification of seizures. Ic tal evolution is typically a dynamic process with signals composed of multiple frequencies; this can limit or complicate methods of analysis . The recently-developed matching pursuit algorithm permits continuous time-frequency analyses, making it particularly appealing for applica tion to these signals. The studies here represent the initial applicat ions of this method to intracranial ictal recordings. Methods: Mesial temporal onset partial seizures were recorded from 9 patients. The dat a were analyzed by the matching pursuit algorithm were continuous digi tized single channel recordings from the depth electrode contact neare st the region of seizure onset. Time-frequency energy distributions we re plotted for each seizure and correlated with the intracranial EEG r ecordings. Results: Periods of seizure initiation, transitional rhythm ic bursting activity, organized rhythmic bursting activity and intermi ttent bursting activity were identified. During periods of organized r hythmic bursting activity, all mesial temporal onset seizures analyzed had a maximum predominant frequency of 5.3-8.4 Hz with a monotonic de cline in frequency over a period of less than 60 s. The matching pursu it method allowed for time-frequency decomposition of entire seizures. Conclusions: The matching pursuit method is a valuable tool for time- frequency analyses of dynamic seizure activity. It is well suited for application to the non-stationary activity that typically characterize s seizure evolution. Time-frequency patterns of seizures originating f rom different brain regions can be compared using the matching pursuit method. (C) 1998 Elsevier Science Ireland Ltd. All rights reserved.