Parameter estimation of slow potassium dynamics in a neuron model for seizure-like activity via adaptive lag synchronization and unscented Kalman filter

Chunxiao Han, Yaru Yang, Tingting Yang, Yingmei Qin, Yanqiu Che

Research output: Contribution to journalArticle

Abstract

We introduce a method that combines the unscented Kalman filter (UKF) and the adaptive lag synchronization (ALS) to estimate the unknown parameters of a neuron model with seizure-like activity using only the heavily noise-corrupted time series of membrane potentials. Although both UKF and ALS are able to estimate the parameters, UKF performs worse when the number of unknown parameters increases, while ALS requires system states that cannot be measured in practice. Therefore, we incorporate UKF as an observer of the unmeasured states into ALS method to estimate multiple parameters. The effectiveness of the combined method is guaranteed by Lyapunov stability theorem and Barbalat's lemma in theory. Numerical simulations demonstrate that, when two parameters are estimated simultaneously, the combined approach has better performance and higher accuracy than only using UKF or ALS method. This exploration of the proposed approach may play an important role in studying new treatments in seizure control.

Original languageEnglish (US)
Article number1950159
JournalInternational Journal of Modern Physics B
Volume33
Issue number15
DOIs
StatePublished - Jun 20 2019

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seizures
Kalman filters
neurons
synchronism
potassium
time lag
estimates
theorems
membranes
simulation

All Science Journal Classification (ASJC) codes

  • Statistical and Nonlinear Physics
  • Condensed Matter Physics

Cite this

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abstract = "We introduce a method that combines the unscented Kalman filter (UKF) and the adaptive lag synchronization (ALS) to estimate the unknown parameters of a neuron model with seizure-like activity using only the heavily noise-corrupted time series of membrane potentials. Although both UKF and ALS are able to estimate the parameters, UKF performs worse when the number of unknown parameters increases, while ALS requires system states that cannot be measured in practice. Therefore, we incorporate UKF as an observer of the unmeasured states into ALS method to estimate multiple parameters. The effectiveness of the combined method is guaranteed by Lyapunov stability theorem and Barbalat's lemma in theory. Numerical simulations demonstrate that, when two parameters are estimated simultaneously, the combined approach has better performance and higher accuracy than only using UKF or ALS method. This exploration of the proposed approach may play an important role in studying new treatments in seizure control.",
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Parameter estimation of slow potassium dynamics in a neuron model for seizure-like activity via adaptive lag synchronization and unscented Kalman filter. / Han, Chunxiao; Yang, Yaru; Yang, Tingting; Qin, Yingmei; Che, Yanqiu.

In: International Journal of Modern Physics B, Vol. 33, No. 15, 1950159, 20.06.2019.

Research output: Contribution to journalArticle

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AU - Che, Yanqiu

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