A new algorithm for epilepsy seizure onset detection and spread estimation from EEG signals

20th Argentinean Bioengineering Society Congress

物理学;生物科学

Quintero-Rincón, Antonio^1 ; Pereyra, Marcelo^2 ; D'Giano, Carlos^3 ; Batatia, Hadj^4 ; Risk, Marcelo^1,5

Department of Bioengineering, Instituto Tecnologico de Buenos Aires (ITBA), Av. Eduardo Madero 399, Buenos Aires

C1106ACD, Argentina^1

Department of Mathematics, University of Bristol, University Walk, Clifton, Bristol

BS8 1TW, United Kingdom^2

Fundación Contra Las Enfermedades Neurológicas Infantiles (FLENI), Argentina^3

University of Toulouse, IRIT - INP-ENSEEIHT, rue Charles Camichel, Toulouse, 2 Cedex 7

31071, France^4

Consejo Nacional de Investigaciones Cientificas y Técnicas (CONICET), Argentina^5

关键词: Cerebral cortex;    EEG signals;    Frequency signals;    Generalized Gaussian modeling;    Physical characterization;    Public health issues;    Seizure onset;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/705/1/012032/pdf
DOI  :  10.1088/1742-6596/705/1/012032

学科分类：生物科学（综合）

来源: IOP

【 摘 要 】

Appropriate diagnosis and treatment of epilepsy is a main public health issue. Patients suffering from this disease often exhibit different physical characterizations, which result from the synchronous and excessive discharge of a group of neurons in the cerebral cortex. Extracting this information using EEG signals is an important problem in biomedical signal processing. In this work we propose a new algorithm for seizure onset detection and spread estimation in epilepsy patients. The algorithm is based on a multilevel 1-D wavelet decomposition that captures the physiological brain frequency signals coupled with a generalized gaussian model. Preliminary experiments with signals from 30 epilepsy crisis and 11 subjects, suggest that the proposed methodology is a powerful tool for detecting the onset of epilepsy seizures with his spread across the brain.