The severe neurological disorder epilepsy affects almost 1% of the world population. For patients
who suffer from pharmacoresistant focal-onset epilepsy, electroencephalographic (EEG) recordings
are essential for the localization of the brain area where seizures start. Apart from the visual
inspection of the recordings, quantitative EEG signal analysis techniques proved to be useful for
this purpose. Among other features, regularity versus irregularity and phase coherence versus phase
independence ...
The severe neurological disorder epilepsy affects almost 1% of the world population. For patients
who suffer from pharmacoresistant focal-onset epilepsy, electroencephalographic (EEG) recordings
are essential for the localization of the brain area where seizures start. Apart from the visual
inspection of the recordings, quantitative EEG signal analysis techniques proved to be useful for
this purpose. Among other features, regularity versus irregularity and phase coherence versus phase
independence allowed characterizing brain dynamics from the measured EEG signals. Can phase
irregularities also characterize brain dynamics? To address this question, we use the univariate
coefficient of phase velocity variation, defined as the ratio of phase velocity standard deviation
and the mean phase velocity. Beyond that, as a bivariate measure we use the classical mean
phase coherence to quantify the degree of phase locking. All phase-based measures are combined
with surrogates to test null hypotheses about the dynamics underlying the signals. In the first
part of our analysis, we use the R¨ossler model system to study our approach under controlled
conditions. In the second part, we use the Bern-Barcelona EEG database which consists of focal
and nonfocal signals extracted from seizure-free recordings. Focal signals are recorded from brain
areas where the first seizure EEG signal changes can be detected, and nonfocal signals are recorded
from areas that are not involved in the seizure at its onset. Our results show that focal signals
have less phase variability and more phase coherence than nonfocal signals. Once combined with
surrogates, the mean phase velocity proved to have the highest discriminative power between
focal and nonfocal signals. In conclusion, conceptually simple and easy to compute phase-based
measures can help to detect features induced by epilepsy from EEG signals. This holds not only
for the classical mean phase coherence but even more so for univariate measures of phase irregularity.
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