Modelling Absence Epilepsy Seizure Data in the NeuCube Evolving Spiking Neural Network Architecture

被引：0

作者：

Capecci, Elisa ^{[1
]}

Espinosa-Ramos, Josafath I. ^{[2
]}

Mammone, Nadia ^{[3
]}

Kasabov, Nikola ^{[1
]}

Duun-Henriksen, Jonas ^{[4
]}

Kjaer, Troels Wesenberg ^{[5
]}

Campolo, Maurizio ^{[6
]}

La Foresta, Fabio ^{[6
]}

Morabito, Francesco C. ^{[6
]}

机构：

[1] Auckland Univ Technol, Knowledge Engn & Discovery Res Inst, AUT Tower,Level 7,Cnr Rutland & Wakefield St, Auckland 1010, New Zealand

[2] Natl Polytech Inst, Ctr Res Comp, Mexico City 07738, DF, Mexico

[3] IRCCS Ctr Neurolesi Bonino Pulejo, Messina, Italy

[4] HypoSafe AS, DK-2800 Lyngby, Denmark

[5] Roskilde Univ Hosp, Dept Neurol, Neurophysiol Ctr, DK-4000 Roskilde, Denmark

[6] Mediterranean Univ Reggio Calabria, DICEAM Dept, I-89060 Reggio Di Calabria, Italy

来源：

2015 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) | 2015年

关键词：

Spiking Neural Networks; EEG; NeuCube; Epilepsy; Childhood Absence Seizures;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Epilepsy is the most diffuse brain disorder that can affect people's lives even on its early stage. In this paper, we used for the first time the spiking neural networks (SNN) framework called NeuCube for the analysis of electroencephalography (EEG) data recorded from a person affected by Absence Epileptic (AE), using permutation entropy (PE) features. Our results demonstrated that the methodology constitutes a valuable tool for the analysis and understanding of functional changes in the brain in term of its spiking activity and connectivity. Future applications of the model aim at personalised modelling of epileptic data for the analysis and the event prediction.

引用

页数：8

共 29 条

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