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Open AccessArticle

An Unsupervised Multichannel Artifact Detection Method for Sleep EEG Based on Riemannian Geometry

1
Czech Technical University in Prague, Jugoslávských partyzánů 1580/3, 160 00 Prague, Czech Republic
2
National Institute of Mental Health, Topolová 748, 250 67 Klecany, Czech Republic
3
GIPSA-lab, University Grenoble Alpes, CNRS, Grenoble-INP, 38000 Grenoble, France
4
Charles University, Third Faculty of Medicine, Ruská 2411/87, 100 00 Prague, Czech Republic
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(3), 602; https://doi.org/10.3390/s19030602
Received: 15 December 2018 / Revised: 11 January 2019 / Accepted: 29 January 2019 / Published: 31 January 2019
(This article belongs to the Special Issue Neurophysiological Data Denoising and Enhancement)
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. View Full-Text
Keywords: sleep EEG; artifact detection; Riemannian geometry sleep EEG; artifact detection; Riemannian geometry
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MDPI and ACS Style

Saifutdinova, E.; Congedo, M.; Dudysova, D.; Lhotska, L.; Koprivova, J.; Gerla, V. An Unsupervised Multichannel Artifact Detection Method for Sleep EEG Based on Riemannian Geometry. Sensors 2019, 19, 602. https://doi.org/10.3390/s19030602

AMA Style

Saifutdinova E, Congedo M, Dudysova D, Lhotska L, Koprivova J, Gerla V. An Unsupervised Multichannel Artifact Detection Method for Sleep EEG Based on Riemannian Geometry. Sensors. 2019; 19(3):602. https://doi.org/10.3390/s19030602

Chicago/Turabian Style

Saifutdinova, Elizaveta; Congedo, Marco; Dudysova, Daniela; Lhotska, Lenka; Koprivova, Jana; Gerla, Vaclav. 2019. "An Unsupervised Multichannel Artifact Detection Method for Sleep EEG Based on Riemannian Geometry" Sensors 19, no. 3: 602. https://doi.org/10.3390/s19030602

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