A Fully Unsupervised Clustering on Adaptively Segmented Long-term EEG Data

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F15%3A00234410" target="_blank" >RIV/68407700:21230/15:00234410 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21460/15:00234410 RIV/68407700:21730/15:00234410
Result on the web
<a href="http://link.springer.com/chapter/10.1007%2F978-3-319-19387-8_244" target="_blank" >http://link.springer.com/chapter/10.1007%2F978-3-319-19387-8_244</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-19387-8_244" target="_blank" >10.1007/978-3-319-19387-8_244</a>

Result language
angličtina
Original language name
A Fully Unsupervised Clustering on Adaptively Segmented Long-term EEG Data
Original language description
Several new procedures for efficient processing of long-term EEG recordings has been validated on long-term comatose EEG data. The proposed solution is based on the use of cluster analysis over a set of various features derived from adaptively segmentedEEG data. Special attention was given to utilization of clinically relevant information from multichannel EEG data.
Czech name
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Czech description
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Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
World Congress on Medical Physics and Biomedical Engineering, June 7-12, 2015, Toronto, Canada
ISBN
978-3-319-19387-8
ISSN
1680-0737
e-ISSN
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Number of pages
4
Pages from-to
1002-1005
Publisher name
Springer International Publishing AG
Place of publication
Cham
Event location
Toronto, Canada
Event date
Jun 7, 2015
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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