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Classifier for Detecting Outliers in Epileptic Seizures

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Bulletin of the Russian Academy of Sciences: Physics Aims and scope

Abstract

The authors consider a classifier for detecting seizures on electroencephalogram records. The classifier is based on a one-class support vector machine, due to features of brain activity during epileptic seizures. A transparent feature selection procedure is used to improve the interpretability of the classifier.

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Funding

This work was supported by the federal academic leadership program “Priority 2030” of the RF Ministry of Science and Higher Education. S.A. Kurkin thanks the RF Presidential Grant Council for its support in developing means of classification as part of project MD-590.2022.1.2. V.V. Grubov thanks the RF Presidential Grant Council for its support in developing means of data analysis as part of project MK-2603.2022.1.6.

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Authors and Affiliations

  1. Baltic Center of Neurotechnology and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236041, Kaliningrad, Russia

    V. V. Grubov, N. M. Smirnov & S. A. Kurkin

Authors
  1. V. V. Grubov
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  2. N. M. Smirnov
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  3. S. A. Kurkin
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Corresponding author

Correspondence to V. V. Grubov.

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Conflict of interest. The authors declare that they have no conflicts of interest.

Statement of compliance with standards of research involving humans as subjects. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Informed consent was obtained from all individual participants involved in the study.

Additional information

Translated by V. Selikhanovich

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Grubov, V.V., Smirnov, N.M. & Kurkin, S.A. Classifier for Detecting Outliers in Epileptic Seizures. Bull. Russ. Acad. Sci. Phys. 87, 532–536 (2023). https://doi.org/10.3103/S1062873822701611

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  • DOI: https://doi.org/10.3103/S1062873822701611

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