| Autor: |
Karpov, Oleg E., Afinogenov, Sergey, Grubov, Vadim V., Maksimenko, Vladimir, Korchagin, Sergey, Utyashev, Nikita, Hramov, Alexander E. |
| Předmět: |
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| Zdroj: |
European Physical Journal: Special Topics; May2023, Vol. 232 Issue 5, p673-682, 10p |
| Abstrakt: |
Epilepsy is a neurological disorder distinguished by sudden and unexpected seizures. To diagnose epilepsy, clinicians register the signals of brain electric activity (electroencephalograms, EEG) and extract segments with seizures. It enables characterizing their type and finding an onset zone, a brain area where they originate. This procedure requires manual EEG deciphering, which is slow and necessitates the assistance of machine learning (ML) algorithms. Traditionally, ML handles this issue in a supervised fashion, i.e., after the training on the representative data, it constructs a boundary in the feature space that separates classes. As the number of features grows, this boundary becomes complex and less generalized. The feature space of brain data is high dimensional. The standard recording includes 30 signals and 50 frequencies resulting in 1500 features. Using additional time-domain features may further enlarge the feature space. Thus, selecting appropriate features is a big part of the successful classification. The selection procedure relies on either a data-based mathematical approach (e.g., principal components, PCs) or the expert domain knowledge of data (explainable features, EFs). Here, we demonstrate the benefits of using EFs. For the EEG data of 30 epileptic patients, we trained a RandomForest algorithm using PCs and EFs. The feature importance analysis revealed that explainable features outperform principal components. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
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