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

Bahador, Nooshin; Erikson, Kristo; Laurila, Jouko; Koskenkari, Juha; Ala-Kokko, Tero; Kortelainen, Jukka (2020-08-27)
 
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https://doi.org/10.1109/EMBC44109.2020.9175711

Bahador, Nooshin
Erikson, Kristo
Laurila, Jouko
Koskenkari, Juha
Ala-Kokko, Tero
Kortelainen, Jukka
Institute of Electrical and Electronics Engineers
27.08.2020

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

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doi:https://doi.org/10.1109/EMBC44109.2020.9175711
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https://urn.fi/URN:NBN:fi-fe2020112092122
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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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