National Repository of Grey Literature 17 records found  previous11 - 17  jump to record: Search took 0.01 seconds. 
Reference Signals In Intracranial Eeg: Implementation And Analysis
Uher, Daniel
The idea of an artifact-free brain activity recording has been circling around the scientific world for a few decades. Noise present in brain activity recordings may complicate the process of evaluation and interpretation. For the elimination of such unwanted components, the concept of virtual reference signals is usually used. In this work, the algorithms for reference signal estimation using common average-based method as well as more recent methods based on independent component analysis (ICA) were realized and evaluated on a new set of real clinical data. It was found that the ICA-based algorithms allow obtaining more accurate estimation of the reference signal as compared to the average-based one. Finally, all the methods were implemented into a free installable Python toolbox, which will be publicly available after additional testing on real data.
Reference signals in intracranial EEG: implementation and analysis
Uher, Daniel ; Hejč, Jakub (referee) ; Ronzhina, Marina (advisor)
The idea of a artifact-free brain activity recording has been circling around the scientific world for a few decades. Parasitic phenomenons and unwanted components may significatntly complicate the analysis of intracranial electroencephalographic (iEEG) recordings. However, with the rise of modern technology, new methods for precise removal of noise artifacts started to emerge. Here we use the concept of virtual reference signals for the elimination of such unwanted components. In this work, the algorithms for reference signal estimation using common average based method as well as more recent methods based on independent component analysis (ICA) were realized and evaluated on a variety of iEEG data. It was found that the ICA-based algorithms allow obtaining more accurate estimation of the reference signal as compared to the average-based one. Finally, all the methods were implemented into a open-source Python package đť‘źđť‘’đť‘“đť‘ đť‘–đť‘”, which is publicly available, easy to install and ready to use.
Case study of intracranial EEG records of patients with focal cortical dysplasia type I and II
Balach, J. ; Ježdík, P. ; Čmejla, R. ; Kršek, P. ; Jiruška, Přemysl
In this study we try to find out if it is possible to differentiate type of focal cortical dysplasia by features obtained from intracranial EEG. We compare occurrence and rates of three biomarkers present in epilepsy in patients with focal cortical dysplasia type I and II. Case study is made on long term night records of 6 pediatric patients. Detection of interictal epileptiform discharges and high-frequency oscillations is made by automated algorithms, delta brush are marked visually. Position of lesion and electrodes inside were obtained from MRI. In individual rates were not found difference on significant level. No major significance were found, but as promising seem to be ratio inside to outside rates of high-frequency oscillations and presence of delta brush, which were found only in patients with focal cortical dysplasia type II.
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.
Brain connectivity estimation
Sladký, Vladimír ; Jurčo, Juraj (referee) ; Cimbálník, Jan (advisor)
Epileptic disease is connected with change in activity of neuronal clusters. Brain connectivity analysis deals with statistic interdependencies between different neuronal centres. Earlier studies show that changes in connectivity can be seen near primary epileptic site. What is changing connectivity and its characteristic in interictal recordings are yet to be fully known. In this thesis are analyzed data from intracranial EEG electrodes, positioned in and neighboring areas of epileptic site. Changes in connectivity of epileptic site and its surroundings are observed by nonlinear correlation method. Decrease in connectivity of epileptic site during slow wave sleep was detected on frequencies above 80 Hz. Reduced connectivity was measured on the border of epileptic zone and normal tissue. Observed features are accentuated during sleep. It was also found out that connectivity at the border of epileptic zone apears to have nonlinear property. The results show that physiological processes during sleep are influencing connectivity near epileptic site and decrease in connectivity may be related to nonlinear dependence of neuronal activity at the border of epileptic zone. This study confirms hypothesis of the earlier studies and reveals new facts about connectivity of epileptic site from the perspective of nonlinear processes. Consequent study based on this findings might lead to more precise delineation of epileptic site and to better understanding of processes, which are causing epileptic fits.
Interactive spatial visualisation of EEG parameters from depth intracranial electrodes in CT/MRI images
Trávníček, Vojtěch ; Klimeš, Petr (referee) ; Cimbálník, Jan (advisor)
This semestral thesis deals with visualization of intracranial EEG. In the first part, theoretical basics of EEG is mentioned. After that, image registration, as a needed tool for visualization is described followed by research of methods of visualization of high frequency oscilations from intracranial EEG. Finally, method for visualization of high frequency oscilations from EEG in real MRI patient scans is designed and implemented.
Interactive Spatial Visualisation of EEG Parameters from Depth Intracranial Electrodes in CT/MRI Images
Trávníček, V.
Standard procedure with patients with focal farmacoresistant epilepsy is partial brain resection. Signals from intracranial EEG are analyzed to find the patological area. This paper presents method for interactive spatial visualisation of EEG parameters in native, three-dimensional CT/MRI images as a tool for displaying pathological areas directly in native MRI or CT images. Software with graphical user interface is programmed and implemented at St. Anne’s University Hospital in Brno (FNUSA). This program is also connected to MySQL database, where parameters from EEG analyses are stored.

National Repository of Grey Literature : 17 records found   previous11 - 17  jump to record:
Interested in being notified about new results for this query?
Subscribe to the RSS feed.