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.

Brno Epilepsy Center Department of Neurology St Anne's University Hospital and Medical Faculty of Masaryk University Brno Czech Republic

CEITEC Central European Institute of Technology Masaryk University Brno Czech Republic

Department of Neurology Mayo Clinic Mayo Systems Electrophysiology Laboratory Rochester MN USA

Department of Physiology and Biomedical Engineering Mayo Clinic Rochester MN USA

International Clinical Research Center St Anne's University Hospital Brno Czech Republic

The Czech Academy of Sciences Institute of Scientific Instruments Brno Czech Republic

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