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Cortical-subcortical interactions in EEG data of patients with pharmacoresistant epilepsy
Šíma, Jan ; Králík, Martin (referee) ; Lamoš, Martin (advisor)
This bachelor's thesis deals with the elaboration of a literature search on epilepsy and electroencephalography signals with a focus on patients with drug-resistant epilepsy and the analysis of cortico-subcortical relationships. The theoretical part describes the chapters of epilepsy, electroencephalography, the possibility of pre-processing EEG data and analytical methods, which describe the cortico-subcortical interactions. The practical part contains pre-processing of EEG data, analysis of methods used, data analysis, results, discussion, and conclusion. The data analysis itself is performed by the Phase-amplitude coupling method. The discussion discusses the results, limitations, and other possible connections. The conclusion summarizes the whole bachelor thesis.
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Acoustic analysis of Mozart effect and its effect in patients with epilepsy
Zemánek, Václav ; Mekyska, Jiří (referee) ; Kiska, Tomáš (advisor)
The music, in generaly, can calm down a human internally. The effect of Mozart’s music can even be measured. Students, who listened Mozart’s music, had higher IQ result and epileptiform activity is describing on patients with epilepsy. This master’s thesis is dealing with design of the evaluation system, which can determine music parameters describing epileptiform activity. In the solution is make detailed analysis of the tracks, signal parameterization, description of data processing and make the Pearson correlation analysis. In the final chapter are described music parameters, which suppress epileptiform activity in the women and the man.
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Analysis of epileptogenic tissue response to intracranial electrical stimulation
Formánková, Zuzana ; Klimeš, Petr (referee) ; Cimbálník, Jan (advisor)
This work deals with the methods of intracranial electrical stimulation and their usage in the localization of epileptogenic tissue. The aim of the thesis is to assess, with help of the proposed markers, the reaction of pathological tissue on the electrical stimulation. Among the suitable markers high-frequency oscillations were classified, interictal spikes, changes in the connectivity, and the signal power within the frequency zones. The markers were detected on the iEEG records taken at the Fakultní nemocnice u sv. Anny in Brno. A software in the Python language has been designed for the purpose of analysis and detection; the software uses the detection algorithms of the EPYCOM library. In the final part of the thesis, the occurrence of the markers was analyzed in terms of dependency on the electrical stimulation. The influence of the electrical stimulation on the iEEG records of patients with epilepsy has been proved.
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Detection of High-Frequency EEG Activity in Epileptic Patients
Cimbálník, Jan ; Kremláček, Jan (referee) ; Jiruška,, Přemysl (referee) ; Jurák, Pavel (advisor)
Tato práce se zabývá automatickou detekcí vysokofrekvenčních oscilací jakožto moderního elektrofyziologického biomarkru epileptogenní tkáně v intrakraniálním EEG, jehož vizuální detekce je zdlouhavý proces, který je ovlivněn subjektivitou hodnotitele. Epilepsie je jedním z nejčastějších neurologických onemocnění postihující 1 % obyvatelstva. Přestože jsou přibližně dvě třetiny případů léčitelné farmakologicky, zbylá třetina pacientů je odkázána zejména na léčbu chirurgickým zákrokem, pro nějž je zapotřebí přesně lokalizovat ložisko patologické tkáně. Vysokofrekvenční oscilace jsou v posledním desetiletí studovány pro jejich potenciál lokalizace patologické tkáně. Součástí této práce je shrnutí dosavadního výzkumu vysokofrekvenčních oscilací a výčet detektorů používaných ve výzkumu. V rámci práce byly vyvinuty či vylepšeny tři detektory vysokofrekvenčních oscilací, na jejichž popis navazuje evaluace z hlediska shody s manuální detekcí, přesnosti výpočtu příznaků oscilací a schopnosti lokalizace patologické tkáně. V závěru práce jsou představeny vyvinuté metody vizualizace vysokofrekvenčních výskytu oscilací a stručně uvedeny dosažené vědecké výsledky.
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Establishing Mutual Links among Brain Structures
Klimeš, Petr ; Hlinka,, Jaroslav (referee) ; Krajča,, Vladimír (referee) ; Halámek, Josef (advisor)
The Human brain consists of mutually connected neuronal populations that build anatomically and functionally separated structures. To understand human brain activity and connectivity, it is crucial to describe how these structures are connected and how information is spread. Commonly used methods often work with data from scalp EEG, with a limited number of contacts, and are incapable of observing dynamic changes during cognitive processes or different behavioural states. In addition, connectivity studies almost never analyse pathological parts of the brain, which can have a crucial impact on pathology research and treatment. The aim of this work is connectivity analysis and its evolution in time during cognitive tasks using data from intracranial EEG. Physiological processes in cognitive stimulation and the local connectivity of pathology in the epileptic brain during wake and sleep were analysed. The results provide new insight into human brain physiology research. This was achieved by an innovative approach which combines connectivity methods with EEG spectral power calculation. The second part of this work focuses on seizure onset zone (SOZ) connectivity in the epileptic brain. The results describe the functional isolation of the SOZ from the surrounding tissue, which may contribute to clinical research and epilepsy treatment.
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The influence of deep brain stimulation on the brain connectivity
Horváthová, Ľubica ; Výtvarová, Eva (referee) ; Klimeš, Petr (advisor)
Hĺbková mozgová stimulácia (DBS) predstavuje účinnú liečbu pre pacientov s Parkinsonovou chorobou (PD) alebo farmakorezistentnou epilepsiou. Avšak mechanizmy, ktorými znižuje počet záchvatov a zlepšuje pohyb, zostávajú ešte do značnej miery neznáme. Pre lepšie pochopenie a určenie, v ktorých frekvenčných pásmach je zmena najdôležitejšia, boli urobené porovnania medzi vypnutou a zapnutou DBS pomocou korelačnej metódy a indexu fázového posunu. Jedenásť pacientov s PD a naimplantovanými neurostimulátormi z firiem Medtronic a St.Jude Medical bolo predmetom nahraných dát použitých v tejto práci. Výsledky dokazujú, že zmena konektivity počas DBS nastane a zároveň, že najviac ovplyvňuje najvyššie frekvencie ako beta, nízka gama a vysoká gama. Zmeny v týchto frekvenciách, zodpovedné za motorickú aktivitu, sústredenie a spracovanie informácií, sú v súlade s klinickou teóriou o PD. Počas tejto choroby je patologická beta aktivita hypersynchronizovaná a gama aktivita je znížená práve v motorických oblastiach. Ak sa gama aktivita počas zapnutej stimulácie zvyšuje, fyziologický stav pacientov sa čiastočne znovuobnovuje a tým zlepšuje ich hybnosť. Metódy a výsledky tejto práce budú použité pre ďalší výskum pacientov s PD a epilepsiou.
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Unsupervised Deep Learning Approach for Seizure Onset Zone localization in Epilepsy
Přidalová, Tereza ; Cimbálník, Jan (referee) ; Mehnen, Lars (advisor)
Epilepsy affects about 50 million people worldwide, with one-third of patients being drugresistant and therefore candidates for an invasive brain resection surgery. Brain resection surgery candidates undergo invasive intracranial encephalography (iEEG) monitoring to determine the seizure onset zone (SOZ). Recorded data can span over weeks and need to be manually reviewed by a physician to assess SOZ. This process can be time-consuming and burdensome due to the vast amount of collected data. This work investigates utilisation of an deep autoencoder for unsupervised data exploration and specifically its ability to discriminate between SOZ and non-SOZ (NSOZ) iEEG channels. The data used in this thesis consists of iEEG collected from 33 patients in two institutes (Mayo Clinic, Rochester, Minnesota, USA and St. Anne´s University Hospital, Brno, Czech Republic - FNUSA) who underwent invasive presurgical monitoring. The autoencoder’s capability to discriminate between SOZ and NSOZ was evaluated using a self-learned embedded feature space representation of the autoencoder network. Autoencoder features were compared to previously established biomarkers for SOZ determination. Discrimination capability was evaluated for both autoencoder features and biomarkers using a Naive Bayes classifier and leave-one-out cross-validation. The achieved area under receiver operating characteristic curve (AUROC) was 0.68 for the FNUSA and 0.56 for the Mayo dataset. Performance in discriminating between SOZ and NSOZ electrodes was not significantly different between the investigated autoencoder features and previously established biomarkers. Selecting the better performing classifier for each patient increased the AUROC to 0.75 and 0.64 for the FNUSA and Mayo dataset, respectively. The results suggest that future approaches combining biomarkers and self-learning methods have a potential to improve the SOZ vs NSOZ discrimination capability of unsupervised iEEG exploration systems, and thus to enhance the surgical management of epilepsy.
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