Fuzzy c-Means Algorithm in Automatic Classification of EEG

Result code in 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>
Result on the web
<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>

Result language
angličtina
Original language name
Fuzzy c-Means Algorithm in Automatic Classification of EEG
Original language description
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.
Czech name
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Czech description
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Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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