Multi-Classification of epileptic High Frequency Oscillations using a Time-Frequency image-based CNN - Université de Rennes Accéder directement au contenu
Communication Dans Un Congrès Année : 2022

Multi-Classification of epileptic High Frequency Oscillations using a Time-Frequency image-based CNN

Résumé

High Frequency Oscillations (HFOs) in intracranial ElectroEncephaloGraphic (iEEG) signals are considered as promising biomarkers for localizing the epileptogenic zone. Visual marking of these particular activities is the typical way not only for the identification of HFOs but also for their discrimination from other transient events such as Interictal Epileptic Spikes (IESs). However, this remains a highly time-consuming process. To cope with this issue, several approaches have been already proposed for an automatic detection of HFOs. Most of these approaches are based on machine learning algorithms where relevant features are to be extracted for efficient classification. Looking for these relevant features is however a challenging task and can be avoided by resorting to deep learning. In this paper, a new method for HFOs multi-classification based on a convolutional neural network (CNN) is proposed. The proposed CNN model is based on Time-Frequency representation of HFOs computed using Stockwell transform. The efficiency of the proposed method is confirmed using real iEEG signals and compared with a supervised machine learning approach based on support vector machine (SVM) as classifier. © 2022 IEEE.
Fichier non déposé

Dates et versions

hal-03931079 , version 1 (09-01-2023)

Identifiants

Citer

F. Krikid, A. Karfoul, A. Kachouri, R Le Bouquin Jeannès. Multi-Classification of epileptic High Frequency Oscillations using a Time-Frequency image-based CNN. 19th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2022, May 2022, Sétif, Algeria. pp.663-667, ⟨10.1109/SSD54932.2022.9955931⟩. ⟨hal-03931079⟩
9 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More