EEG stochastic nonlinear oscillator models for Alzheimer's disease

Parham Ghorbanian, Subramanian Ramakrishnan, Hashem Ashrafiuon

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this article, we derive unique stochastic nonlinear coupled oscillator models of EEG signals from an Alzheimer's Disease (AD) study. EEG signals recorded during resting eyes-open (EO) and eyes-closed (EC) conditions in a pilot study with AD patients and age-matched healthy control subjects (CTL) are employed. An optimization scheme is then utilized to match the output of the stochastic Duffing - van der Pol double oscillator network with EEG signals recorded during each condition for AD and CTL subjects. The selected decision variable are the model parameters and noise intensity. While, the selected signal characteristics are power spectral densities in major brain frequency bands and Shannon and sample entropies to match the signal information content and complexity. It is shown that statistically significant unique models represent the EC and EO conditions for both CTL and AD subjects. Moreover, the inclusion of sample entropy in the optimization process significantly enhances the stochastic nonlinear oscillator model performance. The study suggests that EEG signals recorded under different brain states as well as those belonging to a brain disorder such as Alzheimer's disease can be uniquely represented by stochastic nonlinear oscillators paving the way for identification of new discriminants.

Original languageEnglish (US)
Title of host publicationAdaptive and Intelligent Systems Control; Advances in Control Design Methods; Advances in Non-Linear and Optimal Control; Advances in Robotics; Advances in Wind Energy Systems; Aerospace Applications; Aerospace Power Optimization; Assistive Robotics; Automotive 2
Subtitle of host publicationHybrid Electric Vehicles; Automotive 3: Internal Combustion Engines; Automotive Engine Control; Battery Management; Bio Engineering Applications; Biomed and Neural Systems; Connected Vehicles; Control of Robotic Systems
PublisherAmerican Society of Mechanical Engineers
ISBN (Electronic)9780791857243
DOIs
StatePublished - 2015
EventASME 2015 Dynamic Systems and Control Conference, DSCC 2015 - Columbus, United States
Duration: Oct 28 2015Oct 30 2015

Publication series

NameASME 2015 Dynamic Systems and Control Conference, DSCC 2015
Volume1

Other

OtherASME 2015 Dynamic Systems and Control Conference, DSCC 2015
CountryUnited States
CityColumbus
Period10/28/1510/30/15

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