Quantitative analysis of phenotypic elements augments traditional electroclinical classification of common familial epilepsies

Objective Classification of epilepsy into types and subtypes is important for both clinical care and research into underlying disease mechanisms. A quantitative, data‐driven approach may augment traditional electroclinical classification and shed new light on existing classification frameworks. Methods We used latent class analysis, a statistical method that assigns subjects into groups called latent classes based on phenotypic elements, to...