An algorithm for on-line detection of high frequency oscillations related to epilepsy

Authors:
Armando LóPez-Cuevas;Bernardino Castillo-Toledo;Laura Medina-Ceja;Consuelo Ventura-MejíA;Kenia Pardo-PeñA
Affiliations:
Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (CINVESTAV), Unidad Guadalajara, Av. del Bosque 1145, col. El bajío, C.P. 45019, Zapopan, Jalisco, ...;Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (CINVESTAV), Unidad Guadalajara, Av. del Bosque 1145, col. El bajío, C.P. 45019, Zapopan, Jalisco, ...;Laboratorio de Neurofisiología y Neuroquímica, Departamento de Biología Celular y Molecular, CUCBA, Universidad de Guadalajara, Mexico;Laboratorio de Neurofisiología y Neuroquímica, Departamento de Biología Celular y Molecular, CUCBA, Universidad de Guadalajara, Mexico;Laboratorio de Neurofisiología y Neuroquímica, Departamento de Biología Celular y Molecular, CUCBA, Universidad de Guadalajara, Mexico
Venue:
Computer Methods and Programs in Biomedicine
Year:
2013

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Abstract

Recent studies suggest that the appearance of signals with high frequency oscillations components in specific regions of the brain is related to the incidence of epilepsy. These oscillations are in general small in amplitude and short in duration, making them difficult to identify. The analysis of these oscillations are particularly important in epilepsy and their study could lead to the development of better medical treatments. Therefore, the development of algorithms for detection of these high frequency oscillations is of great importance. In this work, a new algorithm for automatic detection of high frequency oscillations is presented. This algorithm uses approximate entropy and artificial neural networks to extract features in order to detect and classify high frequency components in electrophysiological signals. In contrast to the existing algorithms, the one proposed here is fast and accurate, and can be implemented on-line, thus reducing the time employed to analyze the experimental electrophysiological signals.