Objective: The main diagnostic challenge of drug resistant epilepsies is to find epileptogenic zone, which will be enough to remove to reach seizure freedom. These regions(called the Epileptogenic zone (EZ)(1)must be thus precisely defined from anatomo-electro-clinical observations obtained during pre-surgical evaluation. In a large number of cases, this evaluation involves invasive EEG recordings, in particular stereo-electroencephalography(SEEG)(2,3,4).Most of the existing machine methods focus on seizures with fast activity in the onset.Our aim was to create new tool that help recognize epileptogenic zone through all types of seizure onset patterns from stereo-EEG signals.

Background: Defining the EZ from SEEG can be challenging (5).EZ is increasingly recognized as a network of hyperexcitable connected regions generating seizures and secondary leading to ictal spreading in propagation networks (6).In 75% of cases,the seizure onset patterns involve high frequencies, most often in the high beta or gamma band(7,8).Methods for quantifying the EZ have been developed over the past years in order to complement the SEEG interpretation (for review see(9,6).Most of these methods are based on the detection/mapping of high frequencies, they are inefficient to detect slower patterns of onset that account for 20-30% of commonly observed SEEG patterns(10, 8). In this context, other methods based on functional connectivity have been proposed to study focal seizures onset from intracranial recordings(11,12,13,14,15).

Method: We studied seizures from 51 patients, suffering from focal drug-resistant epilepsy associated with malformation of cortical development. We separated seizure onset patterns to slow and fast. We quantified combined epileptogenicity index (cEI), based on a directed connectivity measure (“out-degrees”) and the classical epileptogenicity index (EI). The results were compared with seizure onset zone (SOZ), detected visually. The quality of the detector was quantified by the area under the precision-recall curve. To test differences between measures was used the Friedman test with Bonferroni correction.

Results: cEI showed the best concordance with visual SOZ in both (slow and fast) groups.

Conclusion: cEI may help epileptologist to delineate SOZ in a complex epileptogenic network. As cEI include the very beginning of fast activity during seizure onset and ictal changes in the epileptogenic network.

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MDS Abstracts - https://www.mdsabstracts.org/abstract/new-tool-to-localize-seizure-onset-zone-from-seeg-signal-connectivity-epileptogenicity-index/