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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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Evaluation of cardiac output by bioimpedance method with patients with pacemaker
Soukup, Ladislav ; Cimbálník, Jan (referee) ; Vondra, Vlastimil (advisor)
This thesis deals with the possibility of using impedance cardiography for calculating cardiac output. Kubicek’s, Sramek‘s and Sramek-Bernstein‘s methods are discussed here. These methods were applied to a data set, obtained by measuring on subjects with implanted cardiostimulators. The subjects’ heart rate was being changed by the programing of cardiostimulators. Thanks to this procedure the measured data were not affected by artifacts, connected with the heart rate change caused by a body stress, or other influences. An influence of heart rate on a cardiac output value based on the statistical processing of the data set was studied.
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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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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.
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The classification of epileptogenic tissue after electrical stimulation using machine learning
Formánková, Zuzana ; Mívalt, Filip (referee) ; Cimbálník, Jan (advisor)
This thesis addresses electrophysiological biomarkers of epileptic activity after direct electrical stimulation in the classification of epileptogenic tissue. Suitable biomarkers included high-frequency oscillations, interictal spikes, changes in connectivity and signal power across frequency bands. Biomarkers were detected in iEEG recordings and their response to direct electrical stimulation was analyzed by statistical tests. Biomarker analysis demonstrated the effect of direct electrical stimulation on electrophysiological biomarkers of epileptic activity. Relevant biomarkers were selected by selection methods as signal power in the frequency band 80-250 Hz, relative entropy in the frequency band 250-600 Hz and linear correlation. Machine learning models, namely logistic regression, support vector machines and decision forest, were implemented for epileptogenic tissue classification. The support vector machines method showed the highest sensitivity (70,5 %) among the models, but the overall results were insufficient (PPV 38,5 %, F1 score 42,9 %). Despite the limitations in the performance of the classification models, this work highlights the potential of electrophysiological biomarkers in identifying epileptogenic foci and establishes a foundation for further research in the field.
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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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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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