Low and high frequency oscillations study of ictal EEG with typical absences in children

• Κύριος συγγραφέας: Σαλαγιάννη, Κωνσταντίνα
• Άλλοι συγγραφείς: Salagianni, Konstantina
• Γλώσσα: English
• Έκδοση: 2021
• Θέματα: Electroencephalography (EEG); Epilepsy; Sleep stages; Brain rhythms; Time-frequency domain; High frequency oscillations (HFO); Low frequency oscillations (LFO); Power spectrum; Connectivity; Cross correlation; Coherence; Imaginary coherency; Morlet wavelets; Επιληψία; Στάδια ύπνου; Ηλεκτροεγκεφαλογραφία; Σήματα εγκεφάλου

The current thesis presents a study of the brain activity of children during epileptic seizures in sleep using electro-encephalographic (EEG) recordings, together with the exploratory data analysis steps that can assist neurologists in their research. In the first part of the thesis, the theoretical framework and the problem formulation is presented: In the first chapter, a presentation of the main brain structure and neural activity is included. The second chapter refers to the EEG signals and the different types of the human brain rhythms. In the same chapter, the different sleep stages are also described. The next chapter presents epilepsy as a disease and describes the epileptic EEG signals that are recorded during seizures. The combination of these fields appears to be of great interest to researchers and therefore, the study of epileptic EEG signals during sleep is the main purpose of this thesis. In the second part, the experimental methodology, EEG signals from six children, 4-9 years old, diagnosed with Idiopathic Generalized Epilepsy, during wakefulness, REM, NonREM Stage I, and NonREM Stage II, are analyzed in the time-frequency domain. Connectivity between different brain areas is calculated and depicted topographically using three different metrics: (a) the power spectrum cross correlation (PSCC), (b) the coherence and (c) the imaginary coherency. Due to lack of a sufficient number of available recordings, no statistically sound conclusions could be extracted from our analysis findings, nevertheless this thesis can be considered as a first step towards the development of a time-frequency EEG analysis toolkit, as it presents a complete methodology that can be used by health providers to help them come to a closer understanding of how children’s brain responds during seizures in sleep.