Intracerebral EEG Artifact Identification Using Convolutional Neural Networks

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68081731%3A_____%2F19%3A00507970" target="_blank" >RIV/68081731:_____/19:00507970 - isvavai.cz</a>
Alternative codes found
RIV/00216224:14110/19:00107403 RIV/00159816:_____/19:00071019
Result on the web
<a href="https://link.springer.com/article/10.1007%2Fs12021-018-9397-6" target="_blank" >https://link.springer.com/article/10.1007%2Fs12021-018-9397-6</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s12021-018-9397-6" target="_blank" >10.1007/s12021-018-9397-6</a>

Result language
angličtina
Original language name
Intracerebral EEG Artifact Identification Using Convolutional Neural Networks
Original language description
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.
Czech name
—
Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20601 - Medical engineering

Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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