A New Method for Nonlinear Dynamic Analysis of the Multi-kinetics Neural Mass Model - 21/05/19
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Abstract |
Objective |
The aim was to use a new method to analyze the nonlinear dynamic characteristics of the multi-kinetics neural mass model. We hope that this new method can be as an auxiliary judgment tool for the diagnosis of brain diseases and the identification of brain activity states.
Methods |
We apply the Lorenz plot to analyze the nonlinear dynamic characteristics of electroencephalogram (EEG) signals from the multi-kinetics neural mass models. The standard deviations in two orthogonal directions of the Lorenz plot are further used to quantify the nonlinear dynamic characteristics of EEG signals.
Results |
The results show that the normalized signal frequency power spectrum may not be able to distinguish normal EEG signals and epileptiform spikes, but the Lorenz plot can distinguish the normal EEG signals and epileptiform spikes effectively. For EEG signals with multi-rhythms, the Lorenz plot of all the simulated signals are oval, but the value of SD1/SD2 increases monotonically when the multi-rhythm EEG signals change from low frequency to high frequency.
Conclusion |
The Lorenz plot of EEG signals with different rhythms presents different distribution. It is an effective nonlinear analysis method for EEG signals.
Le texte complet de cet article est disponible en PDF.Graphical abstract |
Highlights |
• | Lorenz plot is used for nonlinear Dynamic Analysis of the Multi-kinetics Neural Mass Model. |
• | The quantitative indices SD1 and SD2 are used to describe the Lorenz plot. |
• | The Lorenz plot of EEG signals with different rhythms presents different distribution. |
Keywords : Multi-kinetics neural mass model, Lorenz plot, EEG
Plan
Vol 40 - N° 3
P. 183-191 - juin 2019 Retour au numéroBienvenue sur EM-consulte, la référence des professionnels de santé.
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