Automated detection of electroencephalography artifacts in human, rodent and canine subjects using machine learning
| Autor: | Suguru Koyama, Joshua Levitt, Steven Kamerling, Carl Y. Saab, Adam Z. Nitenson, Curry James Taylor, Lonne Heijmans, Jason T. Ross |
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| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
Computer science Electroencephalography Machine Learning Rats Sprague-Dawley 03 medical and health sciences 0302 clinical medicine Dogs medicine Sprague dawley rats Animals Humans Eyes open Artifact (error) medicine.diagnostic_test business.industry General Neuroscience Data interpretation Pattern recognition Signal Processing Computer-Assisted Independent component analysis Brain Waves Rats Support vector machine 030104 developmental biology ROC Curve Artificial intelligence business Artifacts 030217 neurology & neurosurgery |
| Zdroj: | Journal of neuroscience methods. 307 |
| ISSN: | 1872-678X |
| Popis: | Background Electroencephalography (EEG) invariably contains extra-cranial artifacts that are commonly dealt with based on qualitative and subjective criteria. Failure to account for EEG artifacts compromises data interpretation. New Method We have developed a quantitative and automated support vector machine (SVM)-based algorithm to accurately classify artifactual EEG epochs in awake rodent, canine and humans subjects. An embodiment of this method also enables the determination of ‘eyes open/closed’ states in human subjects. Results The levels of SVM accuracy for artifact classification in humans, Sprague Dawley rats and beagle dogs were 94.17%, 83.68%, and 85.37%, respectively, whereas 'eyes open/closed' states in humans were labeled with 88.60% accuracy. Each of these results was significantly higher than chance. Comparison with Existing Methods Other existing methods, like those dependent on Independent Component Analysis, have not been tested in non-human subjects, and require full EEG montages, instead of only single channels, as this method does. Conclusions We conclude that our EEG artifact detection algorithm provides a valid and practical solution to a common problem in the quantitative analysis and assessment of EEG in pre-clinical research settings across evolutionary spectra. |
| Databáze: | OpenAIRE |
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