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Multi-classification of high-frequency oscillations in intracranial EEG signals based on CNN and data augmentation

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Abstract

Interictal high-frequency oscillations (HFOs) recorded in intracranial electroencephalographic (iEEG) signals are reliable biomarkers for the epileptogenic zone. Visual identification of these particular events is manually time-consuming and is subject to clinicians’ expertise. Moreover, differentiating them from other transient events such as interictal epileptic spikes (IESs) presents a considerable challenge. Hence, various approaches have been developed with the aim of extracting automatically discriminant features for HFOs and IESs events. Typically, these approaches are based on machine learning (ML) algorithms, but their efficiency strongly depends on the computed features. To address this limitation, we explore deep learning (DL), as a powerful framework, for the classification of HFOs and IESs and propose a novel convolutional neural network (CNN) architecture for HFOs multi-classification. Time–frequency (TF)-based images, computed using the Stockwell transform of the events of interest, are used as inputs to the CNN-based approach. Furthermore, data augmentation (DA) is adopted to improve the generalization of the proposed CNN model. The numerical simulations on epileptic iEEG signals demonstrate that the proposed approach yields superior results when the DA is employed.

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Acknowledgements

The study presented in this paper was conducted as a component of the PHC (Partenariat Hubert Curien) Project CREDIADIC No. 41711PK, CMCU Code 19G1411.

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Authors and Affiliations

  1. AFDE-ENIS, University of Sfax, Street of Soukra, 3038, Sfax, Tunisia

    Fatma Krikid, Sahbi Chaibi & Abdennaceur Kachouri

  2. Univ Rennes, Inserm, LTSI - UMR 1099, 35000, Rennes, France

    Fatma Krikid, Ahmad Karfoul, Amar Kachenoura & Régine Le Bouquin Jeannès

  3. Service de Neurologie, Pôle des Neurosciences, Cliniques de Rennes, CHU de Rennes, 35000, Rennes, France

    Anca Nica

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  1. Fatma Krikid
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Contributions

Conceptualization: [Fatma Krikid]; Methodology: [Fatma Krikid, Ahmad Karfoul]; Formal analysis and investigation: [Fatma Krikid, Ahmad Karfoul]; Writing - original draft preparation: [Fatma Krikid]; Writing - review and editing: [Ahmad Karfoul, Sahbi Chaibi, Régine Le Bouquin Jeannès, Abdennaceur Kachouri]; Resources: [Anca Nica, Amar Kachenoura], Supervision: [Ahmad Karfoul, Régine Le Bouquin Jeannès].

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Correspondence to Fatma Krikid.

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Krikid, F., Karfoul, A., Chaibi, S. et al. Multi-classification of high-frequency oscillations in intracranial EEG signals based on CNN and data augmentation. SIViP 18, 1099–1109 (2024). https://doi.org/10.1007/s11760-023-02808-4

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  • DOI: https://doi.org/10.1007/s11760-023-02808-4

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