Sparse Differential Connectivity Graph of Scalp EEG for Epileptic Patients

Ladan Amini (1, 2) , Sophie Achard (1) , Christian Jutten (1) , Hamid Soltanian-Zadeh (3, 4) , Gholam Ali Hossein-Zadeh (3, 4) , Olivier David (4) , Laurent Vercueil (4)

1 GIPSA-SIGMAPHY - GIPSA - Signal Images Physique
2 CIPCE - Control and Intelligent Processing Center of Excellence
3 CIPCE - Control and Intelligent Processing Center of Excellence - University of Tehran
4 GIN - Grenoble Institut des Neurosciences

The aim of the work is to integrate the information modulation of the inter-relations between EEG scalp measurements of two brain states in a connectivity graph. We present a sparse differential connectivity graph (SDCG) to distinguish the effectively modulated connections between epileptiform and non-epileptiform states of the brain from all the common connections created by noise, artifact, unwanted background activities and their related volume conduction effect. The proposed method is applied on real epileptic EEG data. Clustering the extracted features from SDCG may present valuable information about the epileptiform focus and their relations.

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Ladan Amini, Sophie Achard, Christian Jutten, Hamid Soltanian-Zadeh, Gholam Ali Hossein-Zadeh, et al.. Sparse Differential Connectivity Graph of Scalp EEG for Epileptic Patients. ESANN 2009 - 17th European Symposium on Artificial Neural Networks, Apr 2009, Bruges, Belgium. ⟨hal-00448557⟩