How analyzing the interictal EEG can identify the epilepsy

O. Yu Panischev; S. A. Demin; S. N. Panischeva; J. Bhattacharya

How analyzing the interictal EEG can identify the epilepsy

O. Yu Panischev, S. A. Demin, S. N. Panischeva, J. Bhattacharya

Academy of Music

Research output: Contribution to journal › Journal article › peer-review

11 Citations (Scopus)

Abstract

Here we demonstrate a capability of a method based on the Flicker-Noise Spectroscopy (FNS) in analyzing the epilepsy manifestation in interictal EEG. Generally the seizure free EEG from epileptic patient does not show visual differences from healthy EEG. Analyzing the behavior of FNS-parameters and the structure of 3D-cross correlators allows to discover the differential characteristics of epilepsy. The cerebral cortex electric activity of epilepsy patients have a specific collective dynamics and configuration of FNS-characteristics in comparison with healthy subjects.

Original language	English
Pages (from-to)	230-235
Number of pages	6
Journal	Nonlinear Phenomena in Complex Systems
Volume	18
Issue number	2
Publication status	Published - 24 Aug 2015

User-Defined Keywords

Flicker-noise spectroscopy
Interictal EEG
Synchronization effects
Time series analysis

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This output contributes to the following UN Sustainable Development Goals (SDGs)

Cite this

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title = "How analyzing the interictal EEG can identify the epilepsy",

abstract = "Here we demonstrate a capability of a method based on the Flicker-Noise Spectroscopy (FNS) in analyzing the epilepsy manifestation in interictal EEG. Generally the seizure free EEG from epileptic patient does not show visual differences from healthy EEG. Analyzing the behavior of FNS-parameters and the structure of 3D-cross correlators allows to discover the differential characteristics of epilepsy. The cerebral cortex electric activity of epilepsy patients have a specific collective dynamics and configuration of FNS-characteristics in comparison with healthy subjects.",

keywords = "Flicker-noise spectroscopy, Interictal EEG, Synchronization effects, Time series analysis",

author = "Panischev, {O. Yu} and Demin, {S. A.} and Panischeva, {S. N.} and J. Bhattacharya",

year = "2015",

month = aug,

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language = "English",

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pages = "230--235",

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T1 - How analyzing the interictal EEG can identify the epilepsy

AU - Panischev, O. Yu

AU - Demin, S. A.

AU - Panischeva, S. N.

AU - Bhattacharya, J.

PY - 2015/8/24

Y1 - 2015/8/24

N2 - Here we demonstrate a capability of a method based on the Flicker-Noise Spectroscopy (FNS) in analyzing the epilepsy manifestation in interictal EEG. Generally the seizure free EEG from epileptic patient does not show visual differences from healthy EEG. Analyzing the behavior of FNS-parameters and the structure of 3D-cross correlators allows to discover the differential characteristics of epilepsy. The cerebral cortex electric activity of epilepsy patients have a specific collective dynamics and configuration of FNS-characteristics in comparison with healthy subjects.

AB - Here we demonstrate a capability of a method based on the Flicker-Noise Spectroscopy (FNS) in analyzing the epilepsy manifestation in interictal EEG. Generally the seizure free EEG from epileptic patient does not show visual differences from healthy EEG. Analyzing the behavior of FNS-parameters and the structure of 3D-cross correlators allows to discover the differential characteristics of epilepsy. The cerebral cortex electric activity of epilepsy patients have a specific collective dynamics and configuration of FNS-characteristics in comparison with healthy subjects.

KW - Flicker-noise spectroscopy

KW - Interictal EEG

KW - Synchronization effects

KW - Time series analysis

UR - http://www.j-npcs.org/abstracts/vol2015/v18no2/v18no2p230.html

UR - http://www.scopus.com/inward/record.url?scp=84940185209&partnerID=8YFLogxK

M3 - Journal article

AN - SCOPUS:84940185209

SN - 1561-4085

VL - 18

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EP - 235

JO - Nonlinear Phenomena in Complex Systems

JF - Nonlinear Phenomena in Complex Systems

IS - 2

ER -

How analyzing the interictal EEG can identify the epilepsy

Abstract

User-Defined Keywords

UN SDGs

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