Development of a Seizure Matching System for Clinical Decision Making in Epilepsy Surgery

Background and Objectives: The proportion of patients becoming seizure-free after epilepsy surgery has stagnated. Large multi-center stereo-electroencephalography datasets can potentially allow comparing a new patient to past similar cases and make clinical decisions with the knowledge of how similar cases were treated in the past. However, the complexity of these evaluations makes the manual search for similar patients in a large database impractical. We aim to develop an automated system that electrographically and anatomically matches seizures from a patient to those in a database. In addition, since we do not know what features define seizure similarity, particularly considering the various stereo-electroencephalography implantation schemes, we evaluate the agreement and features among experts in classifying seizure similarity. Methods: We utilized SEEG seizures from consecutive patients who underwent stereo-electroencephalography for epilepsy surgery. Eight international experts evaluated seizure-pair similarity using a four-level similarity score through a graphical user interface. As our primary outcome, we developed and validated an automated seizure matching system by employing a leave-one-expert-out approach. Secondary outcomes included the inter-rater agreement and features for classifying seizure similarity. Results: 320 SEEG seizures from 95 patients were utilized. The seizure matching system achieved an area-under-the-curve of 0.82 (95% CI, 0.819-0.822), indicating its feasibility. Six distinct seizure similarity features were identified and proved effective: onset region, onset pattern, propagation region, duration, extent of spread, and propagation speed. Among these features, the onset region showed the strongest correlation with expert scores (Spearman's rho=0.75, p<0.001). Additionally, the moderate inter-rater agreement confirmed the practicality of our approach: for the four-level classification, median agreement was 73.9% (interquartile range, 7%), and beyond-chance Gwet's kappa was 0.45 (0.16); for the binary classification of similar vs. not related, agreement stood at 71.9% (4.7%) with a kappa of 0.46 (0.13). Discussion: We demonstrate the feasibility and validity of a stereo-electroencephalography seizure matching system across patients, effectively mirroring the expertise of epileptologists. This novel system can identify patients with seizures similar to that of a patient being evaluated, thus optimizing the treatment plan by considering the treatment and the results of treating similar patients in the past, potentially resulting in an improved surgery outcome. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This work was funded by project grants from the Canadian Institutes of Health Research (PJT-175056 to BF, FDN-143208 to JG, and MFE CIHR-IRSC:0633005463 to JT), and Tanenbaum Open Science grant (JT, JG, BF). JT was supported by the Jeanne Timmins Fellowship. BF was supported by a salary award ("Chercheur-boursier clinicien Senior") of the Fonds de Recherche du Quebec-Sante 2021-2023. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Ethics Board of Montreal Neurological Institute-Hospital, McGill University, Canada gave ethical approval for this work. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The data that support the findings of this study are available upon reasonable request and if in accordance with the respective Research Ethics Boards policies.