Publication details

Intracerebral EEG Artifact Identification Using Convolutional Neural Networks

Authors

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

Year of publication 2019
Type Article in Periodical
Magazine / Source NEUROINFORMATICS
MU Faculty or unit

Faculty of Medicine

Citation
Web https://link.springer.com/content/pdf/10.1007%2Fs12021-018-9397-6.pdf
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)
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.
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