Expert-in-the-loop Learning for Sleep EEG Data

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F18%3A00327733" target="_blank" >RIV/68407700:21230/18:00327733 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21460/18:00327733 RIV/68407700:21730/18:00327733
Result on the web
<a href="https://ieeexplore.ieee.org/document/8621557" target="_blank" >https://ieeexplore.ieee.org/document/8621557</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/BIBM.2018.8621557" target="_blank" >10.1109/BIBM.2018.8621557</a>

Result language
angličtina
Original language name
Expert-in-the-loop Learning for Sleep EEG Data
Original language description
This work addresses the area of a computer-assisted sleep staging using a standard scalp EEG recordings and AASM 2012 scoring rules. We focused on real clinical EEG data containing a large amount of artifacts and/or missing electrodes. The sleep-related features were extracted for 30-seconds segments. Power-in-band features were estimated by a method using Continuous Wavelet Transform (CWT). In addition, entropy, spectral entropy, fractal dimensions and statistical features were used as the input of classifiers. Inter-personal differences and the characteristics of extracted features were evaluated for individual sleep classes. Two expert-in-the-loop strategies and three different classifiers were used to classify data into sleep stages. The results were compared with a fully automated classification and with gold standard expert sleep staging. Due to the proposed improvements the final mean classification sensitivity of expertin-the-loop approach was increased up to 18.4%.The implemented solution allows to classify sleep recordings contaminated by a large amount of the naturally occurring artifacts that are impossible to process by traditional automated classification methods.
Czech name
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Czech description
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Project
<a href="/en/project/GA17-20480S" target="_blank" >GA17-20480S: Temporal context in analysis of long-term non-stationary multidimensional signal</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) - Proceedings
ISBN
978-1-5386-5488-0
ISSN
—
e-ISSN
—
Number of pages
7
Pages from-to
2590-2596
Publisher name
IEEE
Place of publication
—
Event location
Madrid
Event date
Dec 3, 2018
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000458654000445

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