Categorization of interictal epileptiform potentials using a graph-theoretic method

Objectives: In patients with epileptic seizures, localization of the source of interictal epileptiform activity is of interest. For correct source localization, a favorable signal to noise ratio is important, and to achieve this, averaging of several epileptiform potentials is often necessary. Before averaging, a careful categorization of epileptiform potentials with different potential distributions is crucial. The aim of this study was to investigate whether a hierarchic, graph-theoretic algorithm could be used for this categorization.

Methods: In 4 patients, 50–100 sharp waves with different surface distributions were categorized independently with the algorithm, and by visual inspection of the traces. As an independent evaluation of the algorithm, a dipole reconstruction was performed for each sharp wave, and the dipole results for the sharp waves from the different automatically obtained categories were compared.

Results: All patients showed a high degree of correspondence between the results of the automatic analysis and the visual estimation. There were clear differences in dipole results between the sharp waves of the different categories obtained from the automatic categorization.

Conclusion: The results indicate that the graph-theoretic categorization algorithm provides a reliable clustering of interictal epileptiform potentials, and that the method may become a useful tool in the pre-averaging categorization of interictal epileptiform potentials prior to source localization.