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@article{1531399,
author = {Nejedly, Petr and Cimbalnik, Jan and Klimes, Petr and Plesinger, Filip and Halamek, Josef and Kremen, Vaclav and Viscor, Ivo and Brinkmann, Benjamin H. and Pail, Martin and Brázdil, Milan and Worrell, Gregory and Jurak, Pavel},
article_location = {TOTOWA},
article_number = {2},
doi = {http://dx.doi.org/10.1007/s12021-018-9397-6},
keywords = {Intracranial EEG (iEEG); Noise detection; Convolutional neural networks (CNN); Artifact probability matrix (APM)},
language = {eng},
issn = {1539-2791},
journal = {NEUROINFORMATICS},
title = {Intracerebral EEG Artifact Identification Using Convolutional Neural Networks},
url = {https://link.springer.com/content/pdf/10.1007%2Fs12021-018-9397-6.pdf},
volume = {17},
year = {2019}
}
TY - JOUR
ID - 1531399
AU - Nejedly, Petr - Cimbalnik, Jan - Klimes, Petr - Plesinger, Filip - Halamek, Josef - Kremen, Vaclav - Viscor, Ivo - Brinkmann, Benjamin H. - Pail, Martin - Brázdil, Milan - Worrell, Gregory - Jurak, Pavel
PY - 2019
TI - Intracerebral EEG Artifact Identification Using Convolutional Neural Networks
JF - NEUROINFORMATICS
VL - 17
IS - 2
SP - 225-234
EP - 225-234
PB - HUMANA PRESS INC
SN - 15392791
KW - Intracranial EEG (iEEG)
KW - Noise detection
KW - Convolutional neural networks (CNN)
KW - Artifact probability matrix (APM)
UR - https://link.springer.com/content/pdf/10.1007%2Fs12021-018-9397-6.pdf
L2 - https://link.springer.com/content/pdf/10.1007%2Fs12021-018-9397-6.pdf
N2 - Manual and semi-automatic identification of artifacts and unwanted physiological signals in large intracerebral electroencephalographic (iEEG) recordings is time consuming and inaccurate. To date, unsupervised methods to accurately detect iEEG artifacts are not available. This study introduces a novel machine-learning approach for detection of artifacts in iEEG signals in clinically controlled conditions using convolutional neural networks (CNN) and benchmarks the method's performance against expert annotations. The method was trained and tested on data obtained from St Anne's University Hospital (Brno, Czech Republic) and validated on data from Mayo Clinic (Rochester, Minnesota, U.S.A). We show that the proposed technique can be used as a generalized model for iEEG artifact detection. Moreover, a transfer learning process might be used for retraining of the generalized version to form a data-specific model. The generalized model can be efficiently retrained for use with different EEG acquisition systems and noise environments. The generalized and specialized model F1 scores on the testing dataset were 0.81 and 0.96, respectively. The CNN model provides faster, more objective, and more reproducible iEEG artifact detection compared to manual approaches.
ER -
NEJEDLY, Petr, Jan CIMBALNIK, Petr KLIMES, Filip PLESINGER, Josef HALAMEK, Vaclav KREMEN, Ivo VISCOR, Benjamin H. BRINKMANN, Martin PAIL, Milan BRÁZDIL, Gregory WORRELL and Pavel JURAK. Intracerebral EEG Artifact Identification Using Convolutional Neural Networks. \textit{NEUROINFORMATICS}. TOTOWA: HUMANA PRESS INC, 2019, vol.~17, No~2, p.~225-234. ISSN~1539-2791. Available from: https://dx.doi.org/10.1007/s12021-018-9397-6.
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