Assessment of Linear and Nonlinear Synchronization Measures for Analyzing EEG in a Mild Epileptic Paradigm

• Published in: IEEE journal of biomedical and health informatics 2009-07, Vol.13 (4), p.433-441
• Main Authors: Sakkalis, V., Giurcaneanu, C.D., Xanthopoulos, P., Zervakis, M.E., Tsiaras, V., Yang, Y., Karakonstantaki, E., Micheloyannis, S.
• Format: Article
• Language: English
• Subjects: Algorithms; Brain; Child; Coherence; Cortical Synchronization - methods; coupling; Drugs; EEG; Electroencephalography; Epilepsy; Epilepsy - physiopathology; fractal patterns; Fractals; Humans; Length measurement; Linear Models; Models, Neurological; Noise robustness; nonlinear analysis; Nonlinear Dynamics; Performance evaluation; Phase measurement; Signal Processing, Computer-Assisted; surrogate data; synchronization; Testing
• ISSN: 1089-7771; 2168-2194; 1558-0032; 2168-2208
• DOI: 10.1109/TITB.2008.923141

Epilepsy is one of the most common brain disorders and may result in brain dysfunction and cognitive disturbances. Epileptic seizures usually begin in childhood without being accommodated by brain damage and are tolerated by drugs that produce no brain dysfunction. In this study, cognitive function is evaluated in children with mild epileptic seizures controlled with common antiepileptic drugs. Under this prism, we propose a concise technical framework of combining and validating both linear and nonlinear methods to efficiently evaluate (in terms of synchronization) neurophysiological activity during a visual cognitive task consisting of fractal pattern observation. We investigate six measures of quantifying synchronous oscillatory activity based on different underlying assumptions. These measures include the coherence computed with the traditional formula and an alternative evaluation of it that relies on autoregressive models, an information theoretic measure known as minimum description length, a robust phase coupling measure known as phase-locking value, a reliable way of assessing generalized synchronization in state-space and an unbiased alternative called synchronization likelihood. Assessment is performed in three stages; initially, the nonlinear methods are validated on coupled nonlinear oscillators under increasing noise interference; second, surrogate data testing is performed to assess the possible nonlinear channel interdependencies of the acquired EEGs by comparing the synchronization indexes under the null hypothesis of stationary, linear dynamics; and finally, synchronization on the actual data is measured. The results on the actual data suggest that there is a significant difference between normal controls and epileptics, mostly apparent in occipital-parietal lobes during fractal observation tests.