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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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Optimizing neural network architecture for EEG processing using evolutionary algorithms
Pijáčková, Kristýna ; Maršálek, Roman (referee) ; Götthans, Tomáš (advisor)
Tato práce se zabývá optimalizací hyperparametrů neuronových sítí pro zpracování EEG signálu pomocí evolučních algoritmů. Využití evolučních optimalizace může snížit závislost na lidské intuici a empirických znalostech při návrhu neuronové sítě a může tak zefektivnit návrh neuronové sítě. V této práci byl navržen genetický algoritmus, který je vhodný pro optimalizaci hyperparametrů i pro hledání neuronové architektury. Tyto metody byly porovnány s referenčním modelem navrženým inženýrem s expertýzou v této oblasti. Data použitá v této práci jsou rozdělena do čtyř kategorií a pocházejí z Fakultní nemocnice svaté Anny v Brně (SAUH) a Mayo kliniky (MAYO) a obsahují iEEG záznamy u pacienta s epilepsií rezistentní na léky, který podstupuje předoperační vyšetření. Metoda hledání neuronové architektury dosáhla výsledků srovnatelných s referenčním modelem. Optimalizovaný model zlepšil F1 skóre oproti originálnímu, empiricky navrženému modelu z 0.9076 na 0.9673 pro data z SAUH a 0.9222 na 0.9400 pro data z Mayo kliniky. Ke zvýšenému skóre přispěla hlavně zvýšená přesnost klasifikace patologických událostí a šumu, která může mít dále pozitivní vliv v aplikacích tohoto modelu v detektoru záchvatů a šumu.
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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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Functional and structural connectivity of human neocortex in epileptosurgery
Šulc, Vlastimil ; Hořínek, Daniel (advisor) ; Doležalová, Irena (referee) ; Tintěra, Jaroslav (referee)
1 ABSTRACT The presented dissertation deals with prognostic factors influencing a favorable postoperative outcome in patients undergoing surgical treatment of epilepsy and the possibilities of improving the methods used in the localization of epileptogenic lesions. This work is based on the results of four published studies. The first study evaluated the factors influencing the long-term outcomes of epilepsy surgery in MRI-negative (nonlesional) extratemporal lobe epilepsy (nETLE). The aim of the study was to evaluate the benefit of non-invasive diagnostic tests and their relationship with a favorable surgical outcome in a group nETLE patients. Univariate analysis showed that localized interictal epileptiform discharges (IEDs) on the scalp EEG were associated with a favorable surgical outcome. Diagnostic difficulty in this group of patients is highlighted by the fact that, although 9 of 24 patients undergoing surgery had a favorable outcome, and only nine of 85 patients with nETLE achieved such a favorable outcome. The second work evaluated the benefit of SPECT (Single Photon Emission Tomography) statistical processing over traditional subtraction methods in patients with MRI-negative temporal lobe epilepsy (nTLE) and MRI-negative extratemporal epilepsy (nETLE). 49 consecutive patients who underwent...
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Functional and structural connectivity of human neocortex in epileptosurgery
Šulc, Vlastimil ; Hořínek, Daniel (advisor) ; Doležalová, Irena (referee) ; Tintěra, Jaroslav (referee)
1 ABSTRACT The presented dissertation deals with prognostic factors influencing a favorable postoperative outcome in patients undergoing surgical treatment of epilepsy and the possibilities of improving the methods used in the localization of epileptogenic lesions. This work is based on the results of four published studies. The first study evaluated the factors influencing the long-term outcomes of epilepsy surgery in MRI-negative (nonlesional) extratemporal lobe epilepsy (nETLE). The aim of the study was to evaluate the benefit of non-invasive diagnostic tests and their relationship with a favorable surgical outcome in a group nETLE patients. Univariate analysis showed that localized interictal epileptiform discharges (IEDs) on the scalp EEG were associated with a favorable surgical outcome. Diagnostic difficulty in this group of patients is highlighted by the fact that, although 9 of 24 patients undergoing surgery had a favorable outcome, and only nine of 85 patients with nETLE achieved such a favorable outcome. The second work evaluated the benefit of SPECT (Single Photon Emission Tomography) statistical processing over traditional subtraction methods in patients with MRI-negative temporal lobe epilepsy (nTLE) and MRI-negative extratemporal epilepsy (nETLE). 49 consecutive patients who underwent...
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