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Detecting epileptic seizures using machine learning and interpretable features of human EEG

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Abstract

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.

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Data availability

The data that support the findings of this study are available from National Medical and Surgical Center named after N. I. Pirogov of Russian Healthcare Ministry but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of National Medical and Surgical Center named after N. I. Pirogov of Russian Healthcare Ministry.

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Acknowledgements

The study was supported by the Project 36-L-22 of the Priority 2030 program of Immanuel Kant Baltic Federal University. VM thanks The President Grant (MD-2824.2022.1.2) in part of formulating research hypothesis. VG thanks The President Grant (MK-2603.2022.1.6 and MD-590.2022.1.2) in part of data analysis.

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Authors and Affiliations

  1. National Medical and Surgical Center named after N. I. Pirogov, Ministry of Healthcare of the Russian Federation, Nizhnyaya Pervomaiskaya Str. 70, Moscow, 105203, Russia

    Oleg E. Karpov & Nikita Utyashev

  2. Faculty of Information Technology and Big Data Analysis, Financial University under the Government of the Russian Federation, Leningradskii Pr. 49, Moscow, 125167, Russia

    Sergey Afinogenov & Sergey Korchagin

  3. Baltic Center for Artificial Intelligence and Neurotechnology, Immanuel Kant Baltic Federal University, Nevskogo Str. 14, Kaliningrad, 236041, Russia

    Vadim V. Grubov, Vladimir Maksimenko & Alexander E. Hramov

  4. Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, Universitetskaya Str. 1, Innopolis, 420500, Russia

    Vladimir Maksimenko

Authors
  1. Oleg E. Karpov
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  2. Sergey Afinogenov
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  3. Vadim V. Grubov
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  4. Vladimir Maksimenko
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  7. Alexander E. Hramov
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by OEK, SA, VVG, VM, SK, NU, and AEH. The first draft of the manuscript was written by VVG, VM, and AEH and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Alexander E. Hramov.

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Karpov, O.E., Afinogenov, S., Grubov, V.V. et al. Detecting epileptic seizures using machine learning and interpretable features of human EEG. Eur. Phys. J. Spec. Top. 232, 673–682 (2023). https://doi.org/10.1140/epjs/s11734-022-00714-3

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