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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00023752%3A_____%2F19%3A43921548" target="_blank" >RIV/00023752:_____/19:43921548 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21230/18:00327733 RIV/68407700:21460/18:00327733 RIV/68407700:21730/18:00327733

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

30103 - Neurosciences (including psychophysiology)

Projekt

—

Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

ISBN

978-1-5386-5488-0

ISSN

—

e-ISSN

—

Počet stran výsledku

7

Strana od-do

2590-2596

Název nakladatele

Institute of Electrical and Electronics Engineers (IEEE)

Místo vydání

New York

Místo konání akce

Madrid, Spain

Datum konání akce

3. 12. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—