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%2F68407700%3A21230%2F18%3A00327733" target="_blank" >RIV/68407700:21230/18:00327733 - isvavai.cz</a>

Nalezeny alternativní kódy

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

20601 - Medical engineering

Projekt

<a href="/cs/project/GA17-20480S" target="_blank" >GA17-20480S: Časový kontext v úloze analýzy dlouhodobého nestacionárního vícerozměrného signálu</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název statě ve sborníku

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) - Proceedings

ISBN

978-1-5386-5488-0

ISSN

—

e-ISSN

—

Počet stran výsledku

7

Strana od-do

2590-2596

Název nakladatele

IEEE

Místo vydání

—

Místo konání akce

Madrid

Datum konání akce

3. 12. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000458654000445