Multivariate resting-state EEG markers differentiate people with epilepsy and functional seizures

Abstract Background Distinguishing epilepsy from functional/dissociative seizures (FDS) is an ongoing diagnostic challenge. Misdiagnosis delays appropriate treatment and puts patients at significant risk. Quantitative analyses of clinical EEG offer a potential avenue for developing decision-support tools in the diagnosis of seizure disorders. Recent work using univariate features demonstrated that reliably identifying diagnostic traits in the presence of confounding factors remains challenging. However, diagnostic information might be available in multivariate features such as network-based measures. Using a well-controlled dataset, we run the first diagnostic accuracy study assessing the potential of multivariate resting-state EEG markers to directly discriminate between a diagnosis of epilepsy and one of FDS at the time when a diagnosis is suspected and prior to treatment initiation. Methods The dataset, previously examined in a published study, includes 148 age- and sex-matched individuals with suspected seizure disorders who were later diagnosed with non-lesional epilepsy (n=75) or FDS (n=73). Eyes-closed, resting-state EEG data used for the analyses were normal on visual inspection, and acquired while participants were medication-free. Functional network measures in the 6-9 Hz range were extracted and machine learning implemented to assess their predictive potential; different model configurations (including varying model types, dimensionality reduction methods, and approaches to enhance feature stability) were tested to identify the most promising approach for future translational implementations. Results Network measures derived from resting-state EEG discriminate between conditions at levels significantly above chance (maximum balanced accuracy: 67.5%). Their sensitivity to epilepsy (81.8%) is consistently higher than their sensitivity to FDS (53.3%). A systematic assessment of model choices indicates that improving the temporal stability of network features through epoch-wise averaging improves classification accuracy (62.6% to 67.5%). Multiple nonlinear model types succeed on the classification problem, with the three-best performing assigning a consistent diagnostic label to 77.5% of the individuals; however, model choice remains a strong determinant of overall classification accuracy. Dimensionality reduction did not provide a significant advantage in our models. Conclusion We establish evidence for the clinical validity of selected network-based markers to discriminate between a diagnosis of non-lesional epilepsy and FDS prior to treatment initiation, highlighting the measures’ potential to support post-test probability estimation in the clinic. Our models, configured to optimise balanced accuracy, classified people with epilepsy more accurately than people with FDS, indicating that these measures are specific to epilepsy and should not be interpreted as markers of a positive diagnosis of FDS.