Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s12021-018-9397-6 | Neuroinformatics |
Keywords | Field | DocType |
Artifact probability matrix (APM),Convolutional neural networks (CNN),Intracranial EEG (iEEG),Noise detection | Intracerebral EEG,Pattern recognition,Convolutional neural network,Computer science,Transfer of learning,Artificial intelligence,Noise detection,Electroencephalography,Machine learning | Journal |
Volume | Issue | ISSN |
17.0 | 2 | 1559-0089 |
Citations | PageRank | References |
2 | 0.50 | 12 |
Authors | ||
12 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Petr Nejedly | 1 | 2 | 0.50 |
| Cimbalnik, J. | 2 | 2 | 1.51 |
| Petr Klimes | 3 | 2 | 1.85 |
| Plesinger, F. | 4 | 3 | 8.70 |
| Halamek, J. | 5 | 3 | 9.72 |
| Václav Křemen | 6 | 15 | 7.24 |
| ivo viscor | 7 | 4 | 5.53 |
| Benjamin Brinkmann | 8 | 101 | 15.65 |
| Martin Pail | 9 | 6 | 1.24 |
| Milan Brázdil | 10 | 23 | 4.78 |
| Gregory A Worrell | 11 | 107 | 18.00 |
| Pavel Jurák | 12 | 5 | 12.45 |