A method for detecting nonlinear determinism in normal and epileptic brain EEG signals
Amir H Meghdadi1, Reza Fazel-Rezai, Yahya Aghakhani
1University of Manitoba, Winnipeg, MB, Canada. meghdadi@ee.umanitoba.ca
Summary
This study introduces a new method to detect determinism in short time series, like electroencephalography (EEG) signals. The robust method successfully identified determinism in both healthy and epileptic brain activity, even with added noise.
Area of Science:
- Neuroscience
- Signal Processing
- Complexity Science
Background:
- * Determinism detection in short time series is challenging.
- * Electroencephalography (EEG) signals contain complex dynamics.
- * Understanding brain signal determinism aids in diagnosing neurological conditions like epilepsy.
Purpose of the Study:
- * To propose a robust method for detecting determinism in short time series.
- * To apply this method to analyze electroencephalography (EEG) signals from healthy and epileptic subjects.
- * To assess the method's resilience to noise.
Main Methods:
- * Singular Value Decomposition (SVD) to characterize signal component trajectories.
- * Calculation of a novel index of determinism.
- * Application to simulated chaotic signals with added white and colored noise.
- * Analysis of intracranial and scalp EEG recordings.
Main Results:
- * The proposed method demonstrates robustness against significant levels of additive noise.
- * Determinism was detected in all analyzed EEG datasets (healthy and epileptic).
- * Higher significance of determinism was observed in intracranial EEG, especially during epileptic seizure activity.
Conclusions:
- * The developed method reliably detects determinism in short EEG time series.
- * Deterministic patterns are present in both healthy and epileptic brain activity.
- * Intracranial EEG, particularly during seizures, exhibits more pronounced deterministic characteristics.
