Type-Specific Seizure Network Analysis For RNS Target Selection in Multifocal Patients

Genchang Peng, Mehrdad Nourani, Jay Harvey

Keywords: Multifocal, responsive neurostimulation, seizure network, stereo-electroencephalography

TL;DR: This paper presents a type-based functional seizure network model using SEEG to identify RNS targets.

Abstract: Responsive neurostimulation (RNS) is an effective= device for patients with multifocal seizures whose ictal foci are independent of each other across left and right hemispheres. Accurate RNS placement is crucial to enhance seizure suppression outcomes. Stereo-electroencephalography (SEEG) is employed before RNS placement to collect deep brain activities and determine stimulation targets. To deal with different seizure types and semiologies for multifocal patients, this paper presents a functional seizure network model using SEEG to identify potential RNS targets. The network nodes are a subset of SEEG contact points, and directional weighted network edges are SEEG correlations quantified by directed transfer function (DTF). The network nodes are then ranked by their strength values, and the top-4 nodes are selected as RNS targets with respect to different seizure types in each hemisphere. The proposed methodology is validated based on four multifocal patients. Consistent results between our computational findings and clinicians’ decisions are observed with 94.4% overlapping ratio.

Track: 4. AI-based clinical decision support systems

Registration Id: YJNRPVFFKSJ

Submission Number: 236