Detection of High-Frequency EEG Activity in Epileptic Patients

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.This work deals with automated detection of high-frequency oscillations as a novel electrophysiologic biomarker of epileptogenic tissue in intracranial EEG. Visual detection of these oscillations is a time-consuming process and is prone to reviewer bias. Epilepsy is one of the most common neurological diseases affecting 1 % of population. Even though two thirds of cases are successfully treated with anti-epileptic drugs, the rest of the patients are dependent mainly on surgical procedure, which requires precise localization of pathologic focus. High-frequency oscillations have been studied over the last decade for their potential to localize the focus of pathological tissue. Initial part of this work is a summary of the current state of high-frequency oscillations research and a detailed list of detectors used in research. Within the scope of this work three high-frequency oscillation detectors were developed or enhanced. The description of the algorithms is followed by detector evaluation with regard to the concordance with expert reviewed events, feature estimation and the ability to correctly localize pathological tissue. The final part of the work provides an overview of developed visualization methods and a short summary of achieved scientific results.

High frequency oscillations in epileptic and non-epileptic human hippocampus during a cognitive task

Hippocampal high-frequency electrographic activity (HFOs) represents one of the major discoveries not only in epilepsy research but also in cognitive science over the past few decades. A fundamental challenge, however, has been the fact that physiological HFOs associated with normal brain function overlap in frequency with pathological HFOs. We investigated the impact of a cognitive task on HFOs with the aim of improving differentiation between epileptic and non-epileptic hippocampi in humans. Hippocampal activity was recorded with depth electrodes in 15 patients with focal epilepsy during a resting period and subsequently during a cognitive task. HFOs in ripple and fast ripple frequency ranges were evaluated in both conditions, and their rate, spectral entropy, relative amplitude and duration were compared in epileptic and non-epileptic hippocampi. The similarity of HFOs properties recorded at rest in epileptic and non-epileptic hippocampi suggests that they cannot be used alone to distinguish between hippocampi. However, both ripples and fast ripples were observed with higher rates, higher relative amplitudes and longer durations at rest as well as during a cognitive task in epileptic compared with non-epileptic hippocampi. Moreover, during a cognitive task, significant reductions of HFOs rates were found in epileptic hippocampi. These reductions were not observed in non-epileptic hippocampi. Our results indicate that although both hippocampi generate HFOs with similar features that probably reflect non-pathological phenomena, it is possible to differentiate between epileptic and non-epileptic hippocampi using a simple odd-ball task

Impact of cognitive stimulation on ripples within human epileptic and non-epileptic hippocampus

Background: Until now there has been no way of distinguishing between physiological and epileptic hippocampal ripples in intracranial recordings. In the present study we addressed this by investigating the effect of cognitive stimulation on interictal high frequency oscillations in the ripple range (80-250 Hz) within epileptic (EH) and non-epileptic hippocampus (NH). Methods: We analyzed depth EEG recordings in 10 patients with intractable epilepsy, in whom hippocampal activity was recorded initially during quiet wakefulness and subsequently during a simple cognitive task. Using automated detection of ripples based on amplitude of the power envelope, we analyzed ripple rate (RR) in the cognitive and resting period, within EH and NH. Results: Compared to quiet wakefulness we observed a significant reduction of RR during cognitive stimulation in EH, while it remained statistically marginal in NH. Further, we investigated the direct impact of cognitive stimuli on ripples (i.e. immediately post-stimulus), which showed a transient statistically significant suppression of ripples in the first second after stimuli onset in NH only. Conclusion: Our results point to a differential reactivity of ripples within EH and NH to cognitive stimulation

Application of automatic classification methods for driver fatigue monitoring

Cílem této práce bylo zpracovat data z projektu "Bdící auto, spící řidič," která byla naměřena v letech 2000 - 2002. Dále z těchto dat získat příznaky, které by byly vhodné pro rozlišení, zda je řidič bdělý či unavený. Poté zhodnotit relevantnost jednotlivých příznaků a vybrané příznaky pak použít pro automatickou klasifikaci únavy a bdělosti řidičů. V teoretické části práce je popsána únava jako fyziologický úkaz, popsány základní principy použitých metod automatické klasifikace a zhotovena rešerše aktuálního stavu řešení dané problematiky. V praktické části je popsán postup experimentu, při kterém byla data získána. Poté následuje samotný popis metodiky této práce, kdy byly signály elektrookulografie a signálu z Hallovy sondy, který reprezentoval natočení volantu, předzpracovány v programu Matlab. Následně pak byly vypočteny příznaky z jednotlivých signálů, vyexportovány do programu MS Excel, kde byly zpracovány do formy grafů a statisticky zhodnoceny. Po vyřazení nerelevantních příznaků byly použity metody automatické klasifikace pro rozlišení bdělosti a únavy.The goal of this thesis was to process data from the project "Wakeful car, sleeping driver," which were acquired during the period of 2000-2002. Furthermore, to obtain attributes from the data which would be appropriate for determination whether the driver is awake or tired. Moreover to evaluate the relevance of the attributes and to use them for automatic fatigue and alertness classification in drivers. The theoretical part describes fatigue as a physiological phenomenon and discusses the current state of research of the issue. The practical part then describes the experiment in which the data was obtained. This is followed by the description of the methodology of this study where signals of elektrooculography and the hall probe, which represented the steering wheel angle, were pre-processed in Matlab. Subsequently, the attributes were calculated from the individual signals and exported to MS Excel, where they were processed into the form of graphs and statistically evaluated. After the removal of irrelevant attributes automatic classification methods were used for discrimination between alertness and fatigue.Institute of Biophysics and Informatics First Faculty of Medicine Charles University in PragueÚstav biofyziky a informatiky 1. LF UK v PrazeFirst Faculty of Medicine1. lékařská fakult

Dependance of sholk wave biological effects on density of cells in point of focus

Ústav biofyziky a informatiky 1. LF UK v PrazeInstitute of Biophysics and Informatics First Faculty of Medicine Charles University in PragueFirst Faculty of Medicine1. lékařská fakult

Application of automatic classification methods for driver fatigue monitoring

The goal of this thesis was to process data from the project "Wakeful car, sleeping driver," which were acquired during the period of 2000-2002. Furthermore, to obtain attributes from the data which would be appropriate for determination whether the driver is awake or tired. Moreover to evaluate the relevance of the attributes and to use them for automatic fatigue and alertness classification in drivers. The theoretical part describes fatigue as a physiological phenomenon and discusses the current state of research of the issue. The practical part then describes the experiment in which the data was obtained. This is followed by the description of the methodology of this study where signals of elektrooculography and the hall probe, which represented the steering wheel angle, were pre-processed in Matlab. Subsequently, the attributes were calculated from the individual signals and exported to MS Excel, where they were processed into the form of graphs and statistically evaluated. After the removal of irrelevant attributes automatic classification methods were used for discrimination between alertness and fatigue

Dependance of sholk wave biological effects on density of cells in point of focus

Ústav biofyziky a informatiky 1. LF UK v PrazeInstitute of Biophysics and Informatics First Faculty of Medicine Charles University in PragueFirst Faculty of Medicine1. lékařská fakult

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