Evaluation of selected recurrence measures in discriminating pre-ictal and inter-ictal periods from epileptic EEG data

TL;DR

This study assesses recurrence-based complexity measures for distinguishing pre-ictal from inter-ictal EEG states, revealing promising results in some patients but requiring further validation on larger datasets.

Contribution

It evaluates the effectiveness of recurrence quantification analysis and recurrence networks in identifying pre-seizure states in invasive EEG data.

Findings

High agreement among measures in detecting seizure precursors

Precursory structures observed in three patients

Further validation needed on larger datasets

Abstract

We investigate the suitability of selected measures of complexity based on recurrence quantification analysis and recurrence networks for an identification of pre-seizure states in multi-day, multi-channel, invasive electroencephalographic recordings from five epilepsy patients. We employ several statistical techniques to avoid spurious findings due to various influencing factors and due to multiple comparisons and observe precursory structures in three patients. Our findings indicate a high congruence among measures in identifying seizure precursors and emphasize the current notion of seizure generation in large-scale epileptic networks. A final judgment of the suitability for field studies, however, requires evaluation on a larger database.