Detecting best lag of embedding for modeling spike-wave discharges from experimental data

被引：0

作者：

Grischenko, Anastasiya A. ^{[1
,2
]}

Sysoeva, Marina V. ^{[2
,3
]}

van Rijn, Clementina M. ^{[4
]}

Sysoev, Ilya V. ^{[1
,2
]}

机构：

[1] Saratov NG Chernyshevskii State Univ, 83 Astrakhanskaya Str, Saratov, Russia

[2] RAS, Inst Radioengn & Elect, Saratov Branch, 38 Zelenaya Str, Saratov, Russia

[3] Yuri Gagarin State Tech Univ Saratov, 77 Politechnicheskaya Str, Saratov, Russia

[4] Radboud Univ Nijmegen, Donders Ctr Cognit, Nijmegen, Netherlands

来源：

SARATOV FALL MEETING 2019: COMPUTATIONS AND DATA ANALYSIS: FROM NANOSCALE TOOLS TO BRAIN FUNCTIONS | 2020年 / 11459卷

基金：

俄罗斯科学基金会;

关键词：

epilepsy; predictive model; embedding lag; time series analysis; local field potentials; GRANGER CAUSALITY; ABSENCE SEIZURES; TIME-SERIES; CONNECTIVITY; DYNAMICS; EPILEPSY; NETWORK; CHAOS;

D O I：

10.1117/12.2563453

中图分类号：

R445 [影像诊断学];

学科分类号：

100207 ;

摘要：

Purpose. Optimal value of the embedding lag calculation is made. Lag is one of empirical parameters of mathematical models, used in autoregressive models for prediction, coupling analysis, signal classification etc. Methods. The first minimum in the dependence of the mutual information function on the time lag was detected. Results. The calculation showed that the optimal lag is about 8 sampling intervals (1/64 s or 1/8 of the characteristic oscillation period for the absence seizures). Discussion. The optimal lag is about 1/8 of the characteristic oscillation period was obtained for both epileptiform and background activity, including preictal and different stages of ictal activity, i.e. this time scale is present in the signal throughout the observation time.

引用

页数：6

共 27 条

[1] Electroencephalographic differences between WAG/Rij and GAERS rat models of absence epilepsy [J].

Akman, Ozlem ;

Demiralp, Tamer ;

Ates, Nurbay ;

Onat, Filiz Yilmaz .

EPILEPSY RESEARCH, 2010, 89 (2-3) :185-193

[2] Modeling nonlinear oscillatory systems and diagnostics of coupling between them using chaotic time series analysis: applications in neurophysiology [J].

Bezruchko, B. P. ;

Ponomarenko, V. I. ;

Prokhorov, M. D. ;

Smirnov, D. A. ;

Tass, P. A. .

PHYSICS-USPEKHI, 2008, 51 (03) :304-310

[3] Beta oscillations in a large-scale sensorimotor cortical network: Directional influences revealed by Granger causality [J].

Brovelli, A ;

Ding, MZ ;

Ledberg, A ;

Chen, YH ;

Nakamura, R ;

Bressler, SL .

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2004, 101 (26) :9849-9854

[4] Genetic animal models for absence epilepsy: A review of the WAG/Rij strain of rats [J].

Coenen, AML ;

van Luijtelaar, ELJM .

BEHAVIOR GENETICS, 2003, 33 (06) :635-655

[5]

Depaulis A, 2006, MODELS OF SEIZURES AND EPILEPSY, P233, DOI 10.1016/B978-012088554-1/50020-7

[6] EEG nonstationarity during intracranially recorded seizures: statistical and dynamical analysis [J].

Dikanev, T ;

Smirnov, D ;

Wennberg, R ;

Velazquez, JLP ;

Bezruchko, B .

CLINICAL NEUROPHYSIOLOGY, 2005, 116 (08) :1796-1807

[7] Mathematical theory of dynamical chaos and its applications: Review Part 2. Spiral chaos of three-dimensional flows [J].

Gonchenko, S., V ;

Gonchenko, A. S. ;

Kazakov, A. O. ;

Kozlov, A. D. ;

Bakhanova, Yu, V .

IZVESTIYA VYSSHIKH UCHEBNYKH ZAVEDENIY-PRIKLADNAYA NELINEYNAYA DINAMIKA, 2019, 27 (05) :7-52

[8] Choosing the optimal model parameters for Granger causality in application to time series with main timescale [J].

Kornilov, Maksim V. ;

Medvedeva, Tatiana M. ;

Bezruchko, Boris P. ;

Sysoev, Ilya V. .

CHAOS SOLITONS & FRACTALS, 2016, 82 :11-21

[9]

Kozlov A.D., 2017, IZV VYSSH UCHEBN ZAV, V25, P4

[10]

Kraskov A, 2004, PHYS REV E, V69, DOI 10.1103/PhysRevE.69.066138

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