Modular framework for detection of inter-ictal spikes in iEEG

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00159816%3A_____%2F17%3A00068081" target="_blank" >RIV/00159816:_____/17:00068081 - isvavai.cz</a>

Výsledek na webu

—

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

Modular framework for detection of inter-ictal spikes in iEEG

Popis výsledku v původním jazyce

Electrical signals measured directly from the brain, specifically signal events known as inter-ictal spikes are one of the essential biomarkers used for an epilepsy diagnosis and research. It is believed that spikes participate in epileptiform process [1] [7]. The inter-ictal spikes can be recorded also by the scalp EEG technique, but for better localization of their source, usually for surgical treatment of epilepsy, it is necessary to acquire intracranial recordings by depth electrodes and/or subdural electrode grids. Recordings are usually acquired in more than hundred channels simultaneously, and recording process runs for several hours per patient. With a common 5 kHz sampling rate that is used in order to allow also detection of other biomarkers such as HFOs [10], the generated data are of enormous size. These data would have to be analyzed by medical doctors - neurologists - manually. Although the gold standard for interictal spike detection has been and still mainly is the manual evaluation, it has been shown that higher consistency of results can be achieved by automated detection algorithms [2]. Detection algorithms can also save enormous amount of work for reviewers and provide a faster data analysis for research or even clinical practice. Several algorithms for spike detection from scalp EEG already exist [9]. But algorithms [5] [2] for spike detection in intracranial EEG (iEEG) are much more scarce and they rarely address computational efficiency and speed [3].

Název v anglickém jazyce

Modular framework for detection of inter-ictal spikes in iEEG

Popis výsledku anglicky

Electrical signals measured directly from the brain, specifically signal events known as inter-ictal spikes are one of the essential biomarkers used for an epilepsy diagnosis and research. It is believed that spikes participate in epileptiform process [1] [7]. The inter-ictal spikes can be recorded also by the scalp EEG technique, but for better localization of their source, usually for surgical treatment of epilepsy, it is necessary to acquire intracranial recordings by depth electrodes and/or subdural electrode grids. Recordings are usually acquired in more than hundred channels simultaneously, and recording process runs for several hours per patient. With a common 5 kHz sampling rate that is used in order to allow also detection of other biomarkers such as HFOs [10], the generated data are of enormous size. These data would have to be analyzed by medical doctors - neurologists - manually. Although the gold standard for interictal spike detection has been and still mainly is the manual evaluation, it has been shown that higher consistency of results can be achieved by automated detection algorithms [2]. Detection algorithms can also save enormous amount of work for reviewers and provide a faster data analysis for research or even clinical practice. Several algorithms for spike detection from scalp EEG already exist [9]. But algorithms [5] [2] for spike detection in intracranial EEG (iEEG) are much more scarce and they rarely address computational efficiency and speed [3].

Druh

O - Ostatní výsledky

CEP obor

—

OECD FORD obor

30210 - Clinical neurology

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů