| Abstract: |  Selected patients with refractory epilepsy can benefit from surgical treatment. The main purpose of presurgical examination is to identify and delineate epileptogenic areas of the brain which should be removed. Those areas are determined according to the spatial distribution of seizure onsets and electrographic phenomena generated in epileptic brain. Interictal epileptiform discharges (IEDs) are one of them, but their specificity to mark epileptogenic tissue is decreased by the fact, that they are also observed outside the epileptogenic areas. To improve the localizing yield of IEDs, identification of specific features of the discharges generated only within the epileptogenic region is required. The main aim of this project was to develop self-clustering algorithm which will discriminate distinct populations of IEDs according to the morphology of their waveforms. Developed algorithm extracts nine basic morphological features of each discharge detected in band-pass filtered (2 60 Hz) intracranial recordings. Principal component analysis is applied on extracted features to reduce their dimension and Gaussian Mixture Distribution method is utilized to assign each discharge to appropriate cluster.
Developed algorithm was tested in the model of intracranial EEG signal and in data recorded in patients who underwent intracranial monitoring. Results demonstrate the ability of the algorithm to separate IEDs into distinguishable clusters. Clinical significance of the morphological clustering needs to be determined in future.
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