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Communication Dans Un Congrès Année : 2022

1D vs 2D convolutional neural networks for scalp high frequency oscillations identification

Résumé

Scalp High Frequency Oscillations (HFOs) are promising biomarkers of epileptogenic zones. Since HFOs visual detection is strenuous, there is a real need to develop accurate HFOs automatic detectors. In this paper, we present a comparative study of two detectors: onedimensional (1D) Convolutional Neural Networks (CNN) running on High-Density Electroencephalograms signals and two dimensional (2D) CNN on time-frequency maps of those signals. Experimental results show that 1DCNN enables easy end-to-end learning of preprocessing, feature extraction and classification modules while achieving competitive performance.
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Dates et versions

hal-03947648 , version 1 (19-01-2023)

Identifiants

  • HAL Id : hal-03947648 , version 1

Citer

Gaëlle Milon-Harnois, Nisrine Jrad, Daniel Schang, Patrick van Bogaert, Pierre Chauvet. 1D vs 2D convolutional neural networks for scalp high frequency oscillations identification. 2022 30th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Oct 2022, Bruges, Belgium. pp.211. ⟨hal-03947648⟩
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