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

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F14%3A00222997" target="_blank" >RIV/68407700:21230/14:00222997 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21460/14:00222997
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Improving the Homogeneity of Classes of EEG Patterns by Fuzzy C-Means Algorithm and Adaptive Segmentation
Original language description
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
Czech name
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Czech description
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Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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