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 language | English (US) |
|---|---|
| Title of host publication | Adaptive 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 publication | Hybrid 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 |
| Publisher | American Society of Mechanical Engineers |
| ISBN (Electronic) | 9780791857243 |
| DOIs | |
| State | Published - Jan 1 2015 |
| Event | ASME 2015 Dynamic Systems and Control Conference, DSCC 2015 - Columbus, United States Duration: Oct 28 2015 → Oct 30 2015 |
Publication series
| Name | ASME 2015 Dynamic Systems and Control Conference, DSCC 2015 |
|---|---|
| Volume | 1 |
Other
| Other | ASME 2015 Dynamic Systems and Control Conference, DSCC 2015 |
|---|---|
| Country | United States |
| City | Columbus |
| Period | 10/28/15 → 10/30/15 |
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EEG stochastic nonlinear oscillator models for Alzheimer's disease. / Ghorbanian, Parham; Ramakrishnan, Subramanian; Ashrafiuon, Hashem.
Adaptive 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: Hybrid 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. American Society of Mechanical Engineers, 2015. (ASME 2015 Dynamic Systems and Control Conference, DSCC 2015; Vol. 1).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - EEG stochastic nonlinear oscillator models for Alzheimer's disease
AU - Ghorbanian, Parham
AU - Ramakrishnan, Subramanian
AU - Ashrafiuon, Hashem
PY - 2015/1/1
Y1 - 2015/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84973325748&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84973325748&partnerID=8YFLogxK
U2 - 10.1115/DSCC2015-9676
DO - 10.1115/DSCC2015-9676
M3 - Conference contribution
T3 - ASME 2015 Dynamic Systems and Control Conference, DSCC 2015
BT - Adaptive 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
PB - American Society of Mechanical Engineers
ER -