Phase irregularity: A conceptually simple and efficient approach to characterize electroencephalographic recordings...

Area of Science:

• Neuroscience
• Signal Processing
• Medical Diagnostics

Background:

• Epilepsy affects nearly 1% of the global population, with pharmacoresistant focal-onset epilepsy requiring precise seizure origin localization.
• Quantitative electroencephalography (EEG) analysis offers advanced methods beyond visual inspection for characterizing brain dynamics.
• Phase-based EEG signal analysis explores regularity, irregularity, and phase locking to understand brain activity.

Purpose of the Study:

• To investigate whether phase irregularities in EEG signals can characterize brain dynamics in epilepsy.
• To introduce and evaluate the univariate coefficient of phase velocity variation and bivariate mean phase coherence for epilepsy analysis.
• To assess the discriminative power of these phase-based measures in distinguishing focal from nonfocal epilepsy signals.

Main Methods:

• Utilized the univariate coefficient of phase velocity variation (standard deviation of phase velocity / mean phase velocity) and mean phase coherence.
• Employed surrogate data testing to validate findings against null hypotheses.
• Analyzed data from the Rössler model system under controlled conditions and the Bern-Barcelona EEG database (seizure-free recordings).

Main Results:

• Focal EEG signals exhibited reduced phase variability and increased phase coherence compared to nonfocal signals.
• The mean phase velocity measure demonstrated the highest discriminative power between focal and nonfocal signals when combined with surrogates.
• Phase-based measures, especially univariate phase irregularity, showed significant potential in detecting epilepsy-related EEG features.

Conclusions:

• Conceptually simple and computationally efficient phase-based measures can aid in detecting epilepsy-induced features in EEG signals.
• Univariate measures of phase irregularity, alongside classical mean phase coherence, are valuable tools for epilepsy diagnosis.
• These findings highlight the utility of advanced EEG signal analysis for improving epilepsy localization and understanding.