Time–frequency analysis using the matching pursuit algorithm applied to seizures originating from the mesial temporal lobe

Abstract

Objectives: The ability to analyze patterns of recorded seizure activity is important in the localization and classification of seizures. Ictal 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 application to these signals. The studies here represent the initial applications of this method to intracranial ictal recordings. Methods: Mesial temporal onset partial seizures were recorded from 9 patients. The data were analyzed by the matching pursuit algorithm were continuous digitized single channel recordings from the depth electrode contact nearest the region of seizure onset. Time–frequency energy distributions were plotted for each seizure and correlated with the intracranial EEG recordings. Results: Periods of seizure initiation, transitional rhythmic bursting activity, organized rhythmic bursting activity and intermittent bursting activity were identified. During periods of organized rhythmic bursting activity, all mesial temporal onset seizures analyzed had a maximum predominant frequency of 5.3–8.4 Hz with a monotonic decline in frequency over a period of less than 60 s. The matching pursuit 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 characterizes seizure evolution. Time–frequency patterns of seizures originating from different brain regions can be compared using the matching pursuit method.