On the Variability of Chaos Indices in Sleep EEG Signals

2018/03/13 by Amin Banitalebi Dehkordi, Dehkordi, Amin Banitalebi, Gholam‐Ali Hossein‐Zadeh +1
Economics, Econometrics and Finance · Neuroscience · Physics and Astronomy · #Chaos control and synchronization #Complex Systems and Time Series Analysis #FOS: Electrical engineering #Neural dynamics and brain function #Signal Processing (eess.SP) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.1803.04606

openalex publication_date 2018/03/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Previous researches revealed the chaotic and nonlinear nature of EEG signal. In this paper we inspected the variability of chaotic indices of the sleep EEG signal such as largest Lyapunov exponent, mutual information, correlation dimension and minimum embedding dimension among different subjects, conditions and sleep stages. Empirical histograms of these indices are obtained from sleep EEG of 31 subjects, showing that, with a good accuracy, these indices in each stage of sleep vary from healthy human subjects to subjects suspected to have sleep-disordered breathing.

Citations

• Practical method for determining the minimum embedding dimension of a scalar time series
• Determining embedding dimension for phase-space reconstruction using a geometrical construction
• Determining Lyapunov exponents from a time series

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