Fuzzy c-Means Algorithm in Automatic Classification of EEG

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21460%2F16%3A00300918" target="_blank" >RIV/68407700:21460/16:00300918 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-319-33609-1_13" target="_blank" >http://dx.doi.org/10.1007/978-3-319-33609-1_13</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-33609-1_13" target="_blank" >10.1007/978-3-319-33609-1_13</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Fuzzy c-Means Algorithm in Automatic Classification of EEG

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 activity on the computer screen and to detect the epileptiform graphoelements and the other pathological activity. The automation of the process is suggested. The procedure is based on processing temporal profiles computed by means of multichannel adaptive segmentation and subsequent classification of detected signal graphoelements. The temporal profiles, function of the class membership in the course of time, reflect the dynamic EEG microstructure and may be used for visual indication of abnormal changes in the EEG using different colors. We will show that Fuzzy c-means (FCM) algorithm can be used for correct classification of epileptic pattern, creating homogeneous compact classes of significant EEG segments.

Název v anglickém jazyce

Fuzzy c-Means Algorithm in Automatic Classification of EEG

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 activity on the computer screen and to detect the epileptiform graphoelements and the other pathological activity. The automation of the process is suggested. The procedure is based on processing temporal profiles computed by means of multichannel adaptive segmentation and subsequent classification of detected signal graphoelements. The temporal profiles, function of the class membership in the course of time, reflect the dynamic EEG microstructure and may be used for visual indication of abnormal changes in the EEG using different colors. We will show that Fuzzy c-means (FCM) algorithm can be used for correct classification of epileptic pattern, creating homogeneous compact classes of significant EEG segments.

Druh

O - Ostatní výsledky

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

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Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2016

Kód důvěrnosti údajů

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