Abstract
In this article, the Electroencephalography (EEG) signal of the human brain is modeled as the output of stochastic non-linear coupled oscillator networks. It is shown that EEG signals recorded under different brain states in healthy as well as Alzheimer's disease (AD) patients may be understood as distinct, statistically significant realizations of the model. EEG signals recorded during resting eyes-open (EO) and eyes-closed (EC) resting 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 by selecting the model physical parameters and noise intensity. The selected signal characteristics are power spectral densities in major brain frequency bands Shannon and sample entropies. These measures allow matching of linear time varying frequency content as well as non-linear signal information content and complexity. The main finding of the work is that statistically significant unique models represent the EC and EO conditions for both CTL and AD subjects. However, it is also shown that the inclusion of sample entropy in the optimization process, to match the complexity of the EEG signal, enhances the stochastic non-linear oscillator model performance.
Highlights
Quantitative analysis of human brain electroencephalography (EEG) recordings aimed at enhancing our understanding of brain injuries and disorders is currently an important research area
Healthy Eyes-Closed and Eyes-Open Results Initially, we studied the models derived for the EC and EO EEG signals of CTL subjects for validation purposes
Alzheimer’s Disease vs. Control Results we studied the models derived for the EC and EO EEG signals of Alzheimer’s disease (AD) subjects
Summary
Quantitative analysis of human brain electroencephalography (EEG) recordings aimed at enhancing our understanding of brain injuries and disorders is currently an important research area. In addition to being useful in diagnosis, such analysis can provide insights into the underlying neurophysiology of the injury or disorder, thereby leading to better treatment and preventive strategies. While no known cure exists, certain medications have shown promise in delaying the symptoms (Dauwels et al, 2010) prompting researchers to seek early diagnosis and intervention strategies. In this context, analysis of the EEG is a potential non-invasive tool that may aid early diagnosis of AD. The use of EEG signal analysis in order to improve the diagnosis of AD is a complex problem where, despite significant advances, a number of fundamental questions remain open (Elgendi et al, 2011)
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