Application of artificial neural networks for analyses of EEG record with semi-automated etalons extraction: A pilot study

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00023752%3A_____%2F16%3A43915395" target="_blank" >RIV/00023752:_____/16:43915395 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21230/16:00304845 RIV/68407700:21460/16:00304845 RIV/68407700:21730/16:00304845 RIV/61989100:27240/16:86097740
Result on the web
<a href="https://link.springer.com/chapter/10.1007%2F978-3-319-44188-7_7" target="_blank" >https://link.springer.com/chapter/10.1007%2F978-3-319-44188-7_7</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-44188-7_7" target="_blank" >10.1007/978-3-319-44188-7_7</a>

Result language
angličtina
Original language name
Application of artificial neural networks for analyses of EEG record with semi-automated etalons extraction: A pilot study
Original language description
Application of artificial neural network (ANN) classification - multilayer perceptron (MLP) with simulated annealing for initialization and genetic algorithm for weight optimization on multi-channel EEG record is presented here. The novelty of the approach lies in the semi-automated etalon extraction. The etalons are suggested by the k-means algorithm and verified/edited by an expert. The whole process of EEG record consists of multichannel adaptive segmentation, feature extraction from segments, semi-automatic process of etalons extraction by the k-means cluster analysis leading to color segment identification and continuing with manual choice of segments for etalons by the expert and feature extraction of chosen etalons. Subsequent classification by ANN leads to unique color identification of segments in the EEG record and additionally in temporal profile. Our goal is to help the physician by mimetic software because the examination of long multichannel EEG is a tedious work.
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
17th International Conference on Engineering Applications of Neural Networks, EANN 2016; Aberdeen; United Kingdom; 2 September 2016 through 5 September 2016
ISBN
978-3-319-44187-0
ISSN
1865-0929
e-ISSN
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Number of pages
14
Pages from-to
94-107
Publisher name
Springer International Publishing
Place of publication
Cham
Event location
Aberdeen; United Kingdo
Event date
Sep 2, 2016
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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