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Nonlinear Signal Processing Techniques Applied to EEG Measurements
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Author(s): Christos L. Papadelis (Aristotle University of Thessaloniki, Greece), Chrysoula Koutidou-Papadeli (Greek Aerospace Medical Association and Space Research, Greece), Panagiotis D. Bamidis (Aristotle University of Thessaloniki, Greece)and Nicos Maglaveras (Aristotle University of Thessaloniki, Greece)
Copyright: 2006
Pages: 15
Source title:
Handbook of Research on Informatics in Healthcare and Biomedicine
Source Author(s)/Editor(s): Athina A. Lazakidou (University of Peloponnese, Greece)
DOI: 10.4018/978-1-59140-982-3.ch041
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Abstract
The electrical activity of the brain is sensitive to its oxygen supply, and electroencephalography (EEG) has been proposed as a suitable measurement to detect brain activity alterations induced by hypoxia. Since, linear processing techniques that have been used so far in hypoxia studies are based on false linearity assumptions about the generation of the EEG signal, there is a definite need for nonlinear approaches to be applied on EEG data derived from hypoxic conditions. The aim of the present study is to compare nonlinear techniques’ effectiveness to identify significant variations in EEG due to hypoxia. EEG data from two channels were derived from ten healthy subjects participated in the present study. Oxygen and nitrogen mixture was used to simulate hypoxic conditions that correspond to an altitude of 25.000 feet. Non-linear measurements such as correlation dimension, approximate entropy, Lyapunov exponent and detrended fluctuation analysis (DFA) parameters were estimated for EEG signals. The results of the present study confirm the effectiveness of nonlinear techniques to identify significant variations in EEG, which reflect alterations in cerebral function induced by cerebral hypoxic conditions.
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