Classification of EEG graphoelements with supervised and unsupervised learning algorithms

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21460%2F14%3A00222998" target="_blank" >RIV/68407700:21460/14:00222998 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.sciencedirect.com/science/article/pii/S1388245713012789" target="_blank" >http://www.sciencedirect.com/science/article/pii/S1388245713012789</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.clinph.2013.12.042" target="_blank" >10.1016/j.clinph.2013.12.042</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Classification of EEG graphoelements with supervised and unsupervised learning algorithms

Popis výsledku v původním jazyce

The electroencephalogram (EEG) provides markers of brain disturbances in the field of epilepsy. In short duration EEG data recordings, the epileptic graphoelements may not manifest. The visual analysis of lengthy signals is a tedious task. It is necessary to track the EEG activity on the computer screen and to detect the epileptiform graphoelements and the other pathological activity. The automation of the process is needed. We will compare the EEG wave classification both by supervised and unsupervisedlearning algorithms. The feasibility to detect the changes in the microstructure of epileptic activity will be verified. The procedure is based on multichannel adaptive segmentation, feature extraction and classification of graphoelements. To take intoaccount the non-stationary behavior of the signal, the features were extracted from segments detected by adaptive segmentation. The features included amplitude variance, parameters describing duration, number of segments, power in the fre

Název v anglickém jazyce

Classification of EEG graphoelements with supervised and unsupervised learning algorithms

Popis výsledku anglicky

The electroencephalogram (EEG) provides markers of brain disturbances in the field of epilepsy. In short duration EEG data recordings, the epileptic graphoelements may not manifest. The visual analysis of lengthy signals is a tedious task. It is necessary to track the EEG activity on the computer screen and to detect the epileptiform graphoelements and the other pathological activity. The automation of the process is needed. We will compare the EEG wave classification both by supervised and unsupervisedlearning algorithms. The feasibility to detect the changes in the microstructure of epileptic activity will be verified. The procedure is based on multichannel adaptive segmentation, feature extraction and classification of graphoelements. To take intoaccount the non-stationary behavior of the signal, the features were extracted from segments detected by adaptive segmentation. The features included amplitude variance, parameters describing duration, number of segments, power in the fre

Druh

O - Ostatní výsledky

CEP obor

JC - Počítačový hardware a software

OECD FORD obor

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Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2014

Kód důvěrnosti údajů

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