Diagnosing Epilepsy with Normal Interictal EEG Using Dynamic Network Models

Objective While scalp EEG is important for diagnosing epilepsy, a single routine EEG is limited in its diagnostic value. Only a small percentage of routine EEGs show interictal epileptiform discharges (IEDs) and overall misdiagnosis rates of epilepsy are 20-30%. We aim to demonstrate how analyzing network properties in EEG recordings can be used to improve the speed and accuracy of epilepsy diagnosis - even in the absence of IEDs. Methods In this multicenter study, we analyzed routine scalp EEGs from 198 patients with suspected epilepsy and normal initial EEGs. The patients’ diagnoses were later confirmed based on an epilepsy monitoring unit (EMU) admission. About 46% ultimately being diagnosed with epilepsy and 54% with non-epileptic conditions. A logistic regression model was trained using spectral and network-derived EEG features to differentiate between epilepsy and non-epilepsy. The model was trained using 10-fold cross-validation on 70% of the data, which was stratified to include equal numbers of epilepsy and non-epilepsy patients in both training and testing groups. The resulting tool was named EpiScalp. Results EpiScalp achieved an area under the curve (AUC) of 0.940. The model had an accuracy of 0.904, a sensitivity of 0.835, and a specificity of 0.963 in classifying patients as having epilepsy or not. Interpretation EpiScalp provides accurate diagnostic aid from a single initial EEG recording, even in more challenging epilepsy cases with normal initial EEGs. This may represent a paradigm shift in epilepsy diagnosis by deriving an objective measure of epilepsy likelihood from previously uninformative EEGs. ### Competing Interest Statement Authors PM, KG, AL, JGM, and SS own equity in a startup company named Neurologic Solutions which may benefit from the findings of this study in the future. ### Funding Statement This study was funded by the Louis B. Thalheimer Fund for Translational Research and the National Institute of Health (award number R35NS132228) ### 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: This study received ethical approval by the institutional review board (IRB) at each contributing center (Johns Hopkins Hospital, Thomas Jefferson University Hospital, University of Pittsburgh Medical Center, and University of Maryland Medical Center). The data presented and used for analysis were deidentified. 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 Data produced in the present study are not currently available.