Expert-in-the-loop Learning for Sleep EEG Data
Popis výsledku
Identifikátory výsledku
Kód výsledku v IS VaVaI
Nalezeny alternativní kódy
RIV/68407700:21460/18:00327733 RIV/68407700:21730/18:00327733
Výsledek na webu
DOI - Digital Object Identifier
Alternativní jazyky
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.
Klasifikace
Druh
D - Stať ve sborníku
CEP obor
—
OECD FORD obor
20601 - Medical engineering
Návaznosti výsledku
Projekt
GA17-20480S: Časový kontext v úloze analýzy dlouhodobého nestacionárního vícerozměrného signálu
Návaznosti
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Ostatní
Rok uplatnění
2018
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
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
Základní informace
Druh výsledku
D - Stať ve sborníku
OECD FORD
Medical engineering
Rok uplatnění
2018