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The classification of epileptogenic tissue after electrical stimulation using machine learning
Formánková, Zuzana ; Mívalt, Filip (referee) ; Cimbálník, Jan (advisor)
This thesis addresses electrophysiological biomarkers of epileptic activity after direct electrical stimulation in the classification of epileptogenic tissue. Suitable biomarkers included high-frequency oscillations, interictal spikes, changes in connectivity and signal power across frequency bands. Biomarkers were detected in iEEG recordings and their response to direct electrical stimulation was analyzed by statistical tests. Biomarker analysis demonstrated the effect of direct electrical stimulation on electrophysiological biomarkers of epileptic activity. Relevant biomarkers were selected by selection methods as signal power in the frequency band 80-250 Hz, relative entropy in the frequency band 250-600 Hz and linear correlation. Machine learning models, namely logistic regression, support vector machines and decision forest, were implemented for epileptogenic tissue classification. The support vector machines method showed the highest sensitivity (70,5 %) among the models, but the overall results were insufficient (PPV 38,5 %, F1 score 42,9 %). Despite the limitations in the performance of the classification models, this work highlights the potential of electrophysiological biomarkers in identifying epileptogenic foci and establishes a foundation for further research in the field.
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