Metody pro analýzu dlouhodobých klinických záznamů intrakraniálního EEG u epileptických pacientů

Abstract

Intracranial EEG (iEEG) is an indispensable method for pre-surgical planning in patients with pharmacoresistant epilepsy. The current clinical standard for the identification of surgical targets is grounded in the analysis of ictal data, while interictal recordings still play a minor role in clinical diagnoses despite representing 99% of presurgical data. This thesis introduces relative entropy as a new feature for the analysis of interictal data to locate the epileptogenic zone, hence the surgical target. It provides analyses of confounding variables such as antiseizure medication, states of vigilance, and proximity to seizures and reveals that states of vigilance modulate feature values and distributions the most. It further investigates novel features of very-fast ripples and ultra-fast ripples and explores their behavior in different states of vigilance. Finally, a novel detection method for ultra-fast oscillations (2-8kHz) was designed and implemented to allow quantitave analyses of this novel phenomena. Together, these results offer new features for the identification of the epileptogenic zone and contribute to a deeper understanding of presurgical iEEG recordings by description of different confounding factors and their influence on iEEG.Intracranial EEG (iEEG) is an indispensable method for pre-surgical planning in patients with pharmacoresistant epilepsy. The current clinical standard for the identification of surgical targets is grounded in the analysis of ictal data, while interictal recordings still play a minor role in clinical diagnoses despite representing 99% of presurgical data. This thesis introduces relative entropy as a new feature for the analysis of interictal data to locate the epileptogenic zone, hence the surgical target. It provides analyses of confounding variables such as antiseizure medication, states of vigilance, and proximity to seizures and reveals that states of vigilance modulate feature values and distributions the most. It further investigates novel features of very-fast ripples and ultra-fast ripples and explores their behavior in different states of vigilance. Finally, a novel detection method for ultra-fast oscillations (2-8kHz) was designed and implemented to allow quantitave analyses of this novel phenomena. Together, these results offer new features for the identification of the epileptogenic zone and contribute to a deeper understanding of presurgical iEEG recordings by description of different confounding factors and their influence on iEEG.