Metody pro analýzu dlouhodobých záznamů invazivních neurofyziologických dat
| but.committee | prof. Ing. Marek Penhaker, Ph.D. (předseda) prof. Ing. Jan Kremláček, Ph.D. (člen) prof. MUDr. Jakub Otáhal, Ph.D. - opponent (člen) doc. MUDr. Martina Bočková, Ph.D. (člen) Mgr. Terezie Filipenská, Ph.D. (člen) Ing. Martin Vítek, Ph.D. (člen) | cs |
| but.defence | Dizertant stručně, jasně a srozumitelně seznámil komisi s průběhem svého výzkumu a výsledky uvedené v dizertační práci. Zodpověděl dotazy oponenta uspokojivě a pohotově reagoval na dotazy ostatních členů komise. | cs |
| but.jazyk | angličtina (English) | |
| but.program | Biomedicínské technologie a bioinformatika | cs |
| but.result | práce byla úspěšně obhájena | cs |
| dc.contributor.advisor | Jurák, Pavel | en |
| dc.contributor.author | Mívalt, Filip | en |
| dc.contributor.referee | Otáhal,, Jakub | en |
| dc.contributor.referee | Varatharajah,, Yogatheesan | en |
| dc.date.accessioned | 2024-12-10T14:03:19Z | |
| dc.date.available | 2024-12-10T14:03:19Z | |
| dc.date.created | 2024 | cs |
| dc.description.abstract | Epilepsy is one of the most common neurological disorders, affecting nearly one percent of the world population. Sleep disruption is a common comorbidity of epilepsy, negatively influencing the lives of those affected. Deep brain stimulation (DBS) is an established therapy for drug-resistant epilepsy, yet its impact on sleep is not fully understood. This dissertation introduces novel tools and algorithms developed for automated sleep analysis of long-term intracranial electroencephalography (iEEG) signals collected using implantable neural stimulating and sensing devices. A distributed brain co-processor system designed for simultaneous electrical brain stimulation and continuous iEEG sensing is introduced in the first part of this thesis. This system enables the collection of long-term iEEG data, which presents an opportunity to investigate brain neurophysiology, epilepsy, sleep, DBS, and their relationships on long-term scales. The core of the dissertation focuses on the development of automated sleep classification algorithms using iEEG recorded using an implantable neural sensing and stimulating (INSS) device implanted in humans. The proposed approach establishes an automated sleep classification strategy using a single channel of iEEG and expert sleep annotations. The results demonstrate accurate sleep classification, even under different DBS paradigms. Developed sleep classifiers were implemented into a novel Brain RISE Platform for long-term tracking of epilepsy and behavior. The Brain RISE Platform suggests that low-frequency DBS might provide a greater seizure reduction and better sleep and memory in five people with epilepsy compared to clinically approved high-frequency stimulation. The dissertation also explores the use of electrical brain impedance as a potential indicator of sleep state-dependent dynamics of the extracellular brain space. The findings suggest that electrical brain impedance may serve as a surrogate to track the glymphatic system and metabolite clearance in the human brain. In summary, this work contributes to the development of novel methods for automated analysis of long-term iEEG data, facilitating research on brain neurophysiology, epilepsy, sleep, DBS, and their interplay. The findings have implications for the development of the next-generation INSS devices, adaptive stimulation strategies, and the development of future therapies. | en |
| dc.description.abstract | Epilepsy is one of the most common neurological disorders, affecting nearly one percent of the world population. Sleep disruption is a common comorbidity of epilepsy, negatively influencing the lives of those affected. Deep brain stimulation (DBS) is an established therapy for drug-resistant epilepsy, yet its impact on sleep is not fully understood. This dissertation introduces novel tools and algorithms developed for automated sleep analysis of long-term intracranial electroencephalography (iEEG) signals collected using implantable neural stimulating and sensing devices. A distributed brain co-processor system designed for simultaneous electrical brain stimulation and continuous iEEG sensing is introduced in the first part of this thesis. This system enables the collection of long-term iEEG data, which presents an opportunity to investigate brain neurophysiology, epilepsy, sleep, DBS, and their relationships on long-term scales. The core of the dissertation focuses on the development of automated sleep classification algorithms using iEEG recorded using an implantable neural sensing and stimulating (INSS) device implanted in humans. The proposed approach establishes an automated sleep classification strategy using a single channel of iEEG and expert sleep annotations. The results demonstrate accurate sleep classification, even under different DBS paradigms. Developed sleep classifiers were implemented into a novel Brain RISE Platform for long-term tracking of epilepsy and behavior. The Brain RISE Platform suggests that low-frequency DBS might provide a greater seizure reduction and better sleep and memory in five people with epilepsy compared to clinically approved high-frequency stimulation. The dissertation also explores the use of electrical brain impedance as a potential indicator of sleep state-dependent dynamics of the extracellular brain space. The findings suggest that electrical brain impedance may serve as a surrogate to track the glymphatic system and metabolite clearance in the human brain. In summary, this work contributes to the development of novel methods for automated analysis of long-term iEEG data, facilitating research on brain neurophysiology, epilepsy, sleep, DBS, and their interplay. The findings have implications for the development of the next-generation INSS devices, adaptive stimulation strategies, and the development of future therapies. | cs |
| dc.description.mark | P | cs |
| dc.identifier.citation | MÍVALT, F. Metody pro analýzu dlouhodobých záznamů invazivních neurofyziologických dat [online]. Brno: Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. 2024. | cs |
| dc.identifier.other | 161326 | cs |
| dc.identifier.uri | https://hdl.handle.net/11012/249780 | |
| dc.language.iso | en | cs |
| dc.publisher | Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií | cs |
| dc.rights | Standardní licenční smlouva - přístup k plnému textu bez omezení | cs |
| dc.subject | epilepsy | en |
| dc.subject | deep brain stimulation | en |
| dc.subject | implantable neural stimulators | en |
| dc.subject | automated sleep classi- fication | en |
| dc.subject | electrical brain impedance | en |
| dc.subject | epilepsy | cs |
| dc.subject | deep brain stimulation | cs |
| dc.subject | implantable neural stimulators | cs |
| dc.subject | automated sleep classi- fication | cs |
| dc.subject | electrical brain impedance | cs |
| dc.title | Metody pro analýzu dlouhodobých záznamů invazivních neurofyziologických dat | en |
| dc.title.alternative | Methods for Analysis of Long-Term Invasive Neurophysiology Data | cs |
| dc.type | Text | cs |
| dc.type.driver | doctoralThesis | en |
| dc.type.evskp | dizertační práce | cs |
| dcterms.dateAccepted | 2024-12-05 | cs |
| dcterms.modified | 2024-12-05-14:39:10 | cs |
| eprints.affiliatedInstitution.faculty | Fakulta elektrotechniky a komunikačních technologií | cs |
| sync.item.dbid | 161326 | en |
| sync.item.dbtype | ZP | en |
| sync.item.insts | 2024.12.10 15:03:19 | en |
| sync.item.modts | 2024.12.06 05:31:48 | en |
| thesis.discipline | bez specializace | cs |
| thesis.grantor | Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. Ústav biomedicínského inženýrství | cs |
| thesis.level | Doktorský | cs |
| thesis.name | Ph.D. | cs |
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