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N. Bahador, K. Erikson, J. Laurila, J. Koskenkari, T. Ala-Kokko and J. Kortelainen, "Automatic detection of artifacts in EEG by combining deep learning and histogram contour processing," 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Montreal, QC, Canada, 2020, pp. 138-141, doi: 10.1109/EMBC44109.2020.9175711

Automatic detection of artifacts in EEG by combining deep learning and histogram contour processing

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Author: Bahador, Nooshin1; Erikson, Kristo2,3; Laurila, Jouko2,3;
Organizations: 1Physiological Signal Analysis Team, Center for Machine Vision and Signal Analysis, MRC Oulu, University of Oulu, Oulu, Finland
2Research Group of Surgery, Anaesthesiology and Intensive Care, Medical Faculty, University of Oulu, Oulu, Finland
3Division of Intensive Care Medicine, MRC Oulu, University of Oulu and Oulu University Hospital, Oulu, Finland
4Cerenion Oy, Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 1.5 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2020112092122
Language: English
Published: Institute of Electrical and Electronics Engineers, 2020
Publish Date: 2020-11-20
Description:

Abstract

This paper introduces a simple approach combining deep learning and histogram contour processing for automatic detection of various types of artifact contaminating the raw electroencephalogram (EEG). The proposed method considers both spatial and temporal information of raw EEG, without additional need for reference signals like ECG or EOG. The proposed method was evaluated with data including 785 EEG sequences contaminated by artifacts and 785 artifact-free EEG sequences collected from 15 intensive care patients. The obtained results showed an overall accuracy of 0.98, representing high reliability of proposed technique in detecting different types of artifacts and being comparable or outperforming the approaches proposed earlier in the literature.

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Series: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
ISSN: 2375-7477
ISSN-E: 1557-170X
ISSN-L: 2375-7477
ISBN: 978-1-7281-1990-8
ISBN Print: 978-1-7281-1991-5
Pages: 138 - 141
Article number: 9175711
DOI: 10.1109/EMBC44109.2020.9175711
OADOI: https://oadoi.org/10.1109/EMBC44109.2020.9175711
Host publication: Bi42nd Annual International Conferences of the IEEE Engineering in Medicine and ology Society, EMBC 2020, 20-24 July 2020, Montreal, QC, Canada
Conference: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
Subjects:
Brain modeling
Electrodes
Electroencephalography
Histograms
Machine learning
Signal analysis
Training
Article
Funding: Orion Pharma is gratefully acknowledged for the unrestricted financial support of the study.
Copyright information: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.