Improving the Homogeneity of Classes of EEG Patterns by Fuzzy C-Means Algorithm and Adaptive Segmentation

Kód výsledku v IS VaVaI

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Nalezeny alternativní kódy

RIV/68407700:21460/14:00222997

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Improving the Homogeneity of Classes of EEG Patterns by Fuzzy C-Means Algorithm and Adaptive Segmentation

Popis výsledku v původním jazyce

The electroencephalogram (EEG) provides sensitive markers of brain disturbances in the field of epilepsy. In short duration EEG data recordings, the epileptic graphoelements may not manifest itself because of the limited length of the signal being recorded (20 minutes). The visual analysis of multichannel signals during video monitoring (24 hours and more) is a tedious task even for an experienced electroencephalographer. It is necessary to track the EEG activity on the computer screen and to detect theepileptiform graphoelements spikes, seizures and the other pathological activity. The automation of the process is suggested. The procedure is based on processing of temporal profiles computed by means of multichannel adaptive segmentation and subsequent classification of detected signal graphoelements. The temporal profiles, functions 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 EE

Název v anglickém jazyce

Improving the Homogeneity of Classes of EEG Patterns by Fuzzy C-Means Algorithm and Adaptive Segmentation

Popis výsledku anglicky

The electroencephalogram (EEG) provides sensitive markers of brain disturbances in the field of epilepsy. In short duration EEG data recordings, the epileptic graphoelements may not manifest itself because of the limited length of the signal being recorded (20 minutes). The visual analysis of multichannel signals during video monitoring (24 hours and more) is a tedious task even for an experienced electroencephalographer. It is necessary to track the EEG activity on the computer screen and to detect theepileptiform graphoelements spikes, seizures and the other pathological activity. The automation of the process is suggested. The procedure is based on processing of temporal profiles computed by means of multichannel adaptive segmentation and subsequent classification of detected signal graphoelements. The temporal profiles, functions 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 EE

Druh

D - Stať ve sborníku

CEP obor

JC - Počítačový hardware a software

OECD FORD obor

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Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2014

Kód důvěrnosti údajů

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

Název statě ve sborníku

YBERC 2014

ISBN

978-80-971697-0-1

ISSN

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e-ISSN

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Počet stran výsledku

5

Strana od-do

99-103

Název nakladatele

STU v Bratislave, FEI

Místo vydání

Bratislava

Místo konání akce

Bratislava

Datum konání akce

2. 7. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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