Artifact Detection in Multichannel Sleep EEG using Random Forest Classifier

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11120%2F18%3A43917758" target="_blank" >RIV/00216208:11120/18:43917758 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21230/18:00328193 RIV/68407700:21460/18:00328193 RIV/68407700:21730/18:00328193
Result on the web
<a href="https://doi.org/10.1109/BIBM.2018.8621374" target="_blank" >https://doi.org/10.1109/BIBM.2018.8621374</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/BIBM.2018.8621374" target="_blank" >10.1109/BIBM.2018.8621374</a>

Result language
angličtina
Original language name
Artifact Detection in Multichannel Sleep EEG using Random Forest Classifier
Original language description
Detection of artifacts in sleep electroencephalography (EEG) is one of the important tasks on the preprocessing step. Despite many algorithms of artifact detection developed through years, many of them lose their benefits in sleep EEG application. This study proposes a method of artifact detection based on a classification of quasi-stationary EEG epochs with random forest classifier. The method was tested on data of three sleep stages and pre-sleep wake EEG. Results showed 16% increase in F-1 for the wake and 9%, 5% and 16% for different sleep stages in comparison to a baseline. All false detection at every presented sleep stage is investigated.
Czech name
—
Czech description
—

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

Article name in the collection
Proceedings 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
ISBN
978-1-5386-5488-0
ISSN
—
e-ISSN
2156-1133
Number of pages
3
Pages from-to
2803-2805
Publisher name
IEEE Computer Society
Place of publication
New Jersey
Event location
Madrid
Event date
Dec 3, 2018
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000458654000494

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