Study of Learning Entropy for onset detection of epileptic seizures in EEG time series

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F16%3A00305512" target="_blank" >RIV/68407700:21220/16:00305512 - isvavai.cz</a>
Result on the web
<a href="http://ieeexplore.ieee.org/document/7727621/" target="_blank" >http://ieeexplore.ieee.org/document/7727621/</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/IJCNN.2016.7727621" target="_blank" >10.1109/IJCNN.2016.7727621</a>

Result language
angličtina
Original language name
Study of Learning Entropy for onset detection of epileptic seizures in EEG time series
Original language description
This paper presents a case study of non-Shannon entropy, i.e. Learning Entropy (LE), for instant detection of onset of epileptic seizures in individual EEG time series. Contrary to entropy methods of EEG evaluation that are based on probabilistic computations, we present the LE-based approach that evaluates the conformity of individual samples of data to the contemporary learned governing law of a learning system and thus LE can detect changes of dynamics on individual samples of data. For comparison, the principle and the results are compared to the Sample Entropy approach. The promising results indicate the LE potentials for feature extraction enhancement for early detection of epileptic seizures on individual-data-sample basis.
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

Article name in the collection
Proceedings of International Joint Conference on Neural Networks 2016
ISBN
9781509006199
ISSN
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e-ISSN
—
Number of pages
4
Pages from-to
3302-3305
Publisher name
IEEE
Place of publication
New York
Event location
Vancouver
Event date
Jul 24, 2016
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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