A correlation-driven mapping for deep learning application in detecting artifacts within the EEG
|Author:||Bahador, Nooshin1; Erikson, Kristo2,3; Laurila, Jouko2,3;|
1hysiological 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
4Physiological Signal Analysis Team, Center for Machine Vision and Signal Analysis, MRC Oulu, University of Oulu, Oulu, Finland
5Cerenion Oy, Oulu, Finland
|Online Access:||PDF Full Text (PDF, 3.5 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe20201216100923
|Publish Date:|| 2021-10-12
Objective: When developing approaches for automatic preprocessing of electroencephalogram (EEG) signals in non-isolated demanding environment such as intensive care unit (ICU) or even outdoor environment, one of the major concerns is varying nature of characteristics of different artifacts in time, frequency and spatial domains, which in turn causes a simple approach to be not enough for reliable artifact removal. Considering this, current study aims to use correlation-driven mapping to improve artifact detection performance.
Approach: A framework is proposed here for mapping signals from multichannel space (regardless of the number of EEG channels) into two-dimensional RGB space, in which the correlation of all EEG channels is simultaneously taken into account, and a deep convolutional neural network (CNN) model can then learn specific patterns in generated 2D representation related to specific artifact.
Main results: The method with a classification accuracy of 92.30% (AUC = 0.96) in a leave-three-subjects-out cross-validation procedure was evaluated using data including 2310 EEG sequences contaminated by artifacts and 2285 artifact-free EEG sequences collected with BrainStatus self-adhesive electrode and wireless amplifier from 15 intensive care patients. For further assessment, several scenarios were also tested including performance variation of proposed method under different segment lengths, different numbers of isoline and different numbers of channel. The results showed outperformance of CNN fed by correlation coefficients data over both spectrogram-based CNN and EEGNet on the same dataset.
Significance: This study showed the feasibility of utilizing correlation image of EEG channels coupled with deep learning as a promising tool for dimensionality reduction, channels fusion and capturing various artifacts patterns in temporal-spatial domains. A simplified version of proposed approach was also shown to be feasible in real-time application with latency of 0.0181 s for making real-time decision.
Journal of neural engineering
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
113 Computer and information sciences
Orion Pharma is gratefully acknowledged for the unrestricted financial support of this study.
© 2020 IOP Publishing Ltd. The Definitive Version of Record can be found online at: https://doi.org/10.1088/1741-2552/abb5bd.