Evaluation of dynamic scaling of growing interfaces in EEG fluctuations of seizures in animal model of temporal lobe epilepsy.

Autor: Martínez-González CL; Instituto Politécnico Nacional, SEPI ESIME-Z, Av. IPN S/N, C.P. 07738, Mexico. Electronic address: lizbeth.martinez@gmail.com., Balankin A; Instituto Politécnico Nacional, SEPI ESIME-Z, Av. IPN S/N, C.P. 07738, Mexico., López T; Universidad Autónoma Metropolitana, C.P. 14387, Mexico., Manjarrez-Marmolejo J; Instituto Nacional de Neurología y Neurocirugía 'MVS', C.P. 14269, Mexico., Martínez-Ortiz EJ; Instituto Politécnico Nacional, SEPI ESIME-Z, Av. IPN S/N, C.P. 07738, Mexico.
Jazyk: angličtina
Zdroj: Computers in biology and medicine [Comput Biol Med] 2017 Sep 01; Vol. 88, pp. 41-49. Date of Electronic Publication: 2017 Jul 04.
DOI: 10.1016/j.compbiomed.2017.07.003
Abstrakt: Epileptic seizures, as a dynamic phenomenon in brain behavior, obey a scale-free behavior, frequently analyzed by electrical activity recording. This recording can be seen as a surface that roughens with time. Dynamic scaling studies roughening processes or growing interfaces. In this theory, a set of exponents -obtained from scale invariance properties- characterize rough interfaces growth. The aim of the present study was to investigate scaling behavior in EEG time series fluctuations of a chemical animal model of temporal lobe epilepsy, with dynamic scaling to detect changes on seizure onset. We analyzed local variables in different sampling intervals and estimated rough, scaling and dynamic exponents. Results exhibited long-range correlations in interictal activity. Results of renormalization and data collapsing confirmed that each epoch of EEG fluctuations for interictal, preictal and postictal collapse in a curve in different scales, each segment independently; remarkably, we found non-scaling behavior in seizures epochs. Data for the different sampling intervals for ictal period do not collapse in one curve, which implies that ictal activity does not exhibit the same scaling behavior than the other epochs. Statistical significant differences of growth exponent were found between interictal and ictal segment, while for scaling exponent, significant differences were found between interictal and postictal segment. These results confirm the potential of scaling exponents as characteristic parameters to detect changes on seizure onset, which suggests their use as inputs for analysis methods for seizure detection in long-term recordings, while changes in growth exponent are potentially useful for prediction purposes.
(Copyright © 2017 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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