[PhD thesis] Nonlinear signal analysis of micro and macro electroencephalographic recordings from epilepsy patients
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[PhD thesis] Nonlinear signal analysis of micro and macro electroencephalographic recordings from epilepsy patients
[PhD thesis] Nonlinear signal analysis of micro and macro electroencephalographic recordings from epilepsy patients
Author: Cristina González Martínez
Supervisor: Ralph Andrzejak
The use of nonlinear signal analysis measures to characterize electroencephalographic (EEG) recordings can be key for a better understanding of the underlying brain dynamics. In neurological disorders such as epilepsy, these dynamics are altered as result of a disturbed coordination between neuronal populations. The aim of this thesis is to characterize the seizure-free interval of EEG recordings from epilepsy patients by means of nonlinear signal analysis techniques to investigate whether this type of analysis can contribute to the localization of the seizure onset zone, the brain region from which initial seizure discharges can be recorded. For this purpose, we used a surrogate-corrected nonlinear predictability score and a surrogatecorrected nonlinear interdependence measure to analyze all-night EEG recordings from epilepsy patients implanted with hybrid depth electrodes equipped with macro contacts and micro wires. Our results show that the combined analysis of macro and micro EEG recordings may help to further increase the degree to which quantitative EEG analysis can contribute to the diagnostics in epilepsy patients.
Link to manuscript: http://hdl.handle.net/10803/670397