An unsupervised multichannel artifact detection method for sleep EEG based on Riemannian geometry

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00023752%3A_____%2F19%3A43919712" target="_blank" >RIV/00023752:_____/19:43919712 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21230/19:00328194 RIV/68407700:21460/19:00328194 RIV/68407700:21730/19:00328194 RIV/00216208:11120/19:43917610

Výsledek na webu

<a href="https://www.mdpi.com/1424-8220/19/3/602" target="_blank" >https://www.mdpi.com/1424-8220/19/3/602</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/s19030602" target="_blank" >10.3390/s19030602</a>

Jazyk výsledku

angličtina

Název v původním jazyce

An unsupervised multichannel artifact detection method for sleep EEG based on Riemannian geometry

Popis výsledku v původním jazyce

In biomedical signal processing, we often face the problem of artifacts that distort the original signals. This concerns also sleep recordings, such as EEG. Artifacts may severely affect or even make impossible visual inspection, as well as automatic processing. Many proposed methods concentrate on certain artifact types. Therefore, artifact-free data are often obtained after sequential application of different methods. Moreover, single-channel approaches must be applied to all channels alternately. The aim of this study is to develop a multichannel artifact detection method for multichannel sleep EEG capable of rejecting different artifact types at once. The inspiration for the study is gained from recent advances in the field of Riemannian geometry. The method we propose is tested on real datasets. The performance of the proposed method is measured by comparing detection results with the expert labeling as a reference and evaluated against a simpler method based on Riemannian geometry that has previously been proposed, as well as against the state-of-the-art method FASTER. The obtained results prove the effectiveness of the proposed method.

Název v anglickém jazyce

An unsupervised multichannel artifact detection method for sleep EEG based on Riemannian geometry

Popis výsledku anglicky

In biomedical signal processing, we often face the problem of artifacts that distort the original signals. This concerns also sleep recordings, such as EEG. Artifacts may severely affect or even make impossible visual inspection, as well as automatic processing. Many proposed methods concentrate on certain artifact types. Therefore, artifact-free data are often obtained after sequential application of different methods. Moreover, single-channel approaches must be applied to all channels alternately. The aim of this study is to develop a multichannel artifact detection method for multichannel sleep EEG capable of rejecting different artifact types at once. The inspiration for the study is gained from recent advances in the field of Riemannian geometry. The method we propose is tested on real datasets. The performance of the proposed method is measured by comparing detection results with the expert labeling as a reference and evaluated against a simpler method based on Riemannian geometry that has previously been proposed, as well as against the state-of-the-art method FASTER. The obtained results prove the effectiveness of the proposed method.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

30404 - Biomaterials (as related to medical implants, devices, sensors)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

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

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název periodika

Sensors

ISSN

1424-8220

e-ISSN

—

Svazek periodika

19

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

15

Strana od-do

"Article Number: 602"

Kód UT WoS článku

000459941200165

EID výsledku v databázi Scopus

2-s2.0-85060906213