Ryvlin, Philippe

Abstract This paper provides a practical guide to applying the updated seizure classification in clinical settings. The updated classification, published by the International League Against Epilepsy in 2025, builds on the operational classification introduced in 2017. It aims to enhance clarity, clinical relevance, and consistency in seizure terminology across various healthcare settings. The classification system distinguishes between four main seizure classes: focal, generalized, unknown whether focal or generalized, and unclassified. The basic version allows for application even in resource‐limited or primary care environments, while the expanded version offers detailed semiological descriptors and supports advanced diagnostic and surgical decision‐making. Consciousness, defined through awareness (recall) and responsiveness, is now considered a classifier. The paper also highlights the importance of distinguishing observable from non‐observable features and integrates semiology into the expanded classification with attention to temporal sequence and somatotopic detail. Generalized seizures are categorized into seizure types having a direct impact on syndrome diagnosis and treatment decisions. Epileptic spasms are given special attention because of their unique presentation and the urgency of early intervention, particularly in infants. To support clinical implementation, this paper includes a comprehensive table of semiological descriptors, definitions of generalized seizure types, and a series of real‐world case vignettes illustrating the application of the updated seizure classification. Supplementary figures and videos further support the educational aims of the paper. This practical guide is intended for healthcare professionals managing patients with epilepsy, providing a clear, structured approach to seizure classification that is adaptable to varying levels of clinical expertise and diagnostic resources.

Abstract The International League Against Epilepsy (ILAE) has updated the operational classification of epileptic seizures, building upon the framework established in 2017. This revision, informed by the implementation experience, involved a working group appointed by the ILAE Executive Committee. Comprising 37 members from all ILAE regions, the group utilized a modified Delphi process, requiring a consensus threshold of more than two thirds for any proposal. Following public comments, the Executive Committee appointed seven additional experts to the revision task force to address and incorporate the issues raised, as appropriate. The updated classification maintains four main seizure classes: Focal, Generalized, Unknown (whether focal or generalized), and Unclassified. Taxonomic rules distinguish classifiers, which are considered to reflect biological classes and directly impact clinical management, from descriptors, which indicate other important seizure characteristics. Focal seizures and those of unknown origin are further classified by the patient's state of consciousness (impaired or preserved) during the seizure, defined operationally through clinical assessment of awareness and responsiveness. If the state of consciousness is undetermined, the seizure is classified under the parent term, that is, the main seizure class (focal seizure or seizure of unknown origin). Generalized seizures are grouped into absence seizures, generalized tonic–clonic seizures, and other generalized seizures, now including recognition of negative myoclonus as a seizure type. Seizures are described in the basic version as with or without observable manifestations, whereas an expanded version utilizes the chronological sequence of seizure semiology. This updated classification comprises four main classes and 21 seizure types. Special emphasis was placed on ensuring translatability into languages beyond English. Its aim is to establish a common language for all health care professionals involved in epilepsy care, from resource‐limited areas to highly specialized centers, and to provide accessible terms for patients and caregivers.

Abstract This educational topical review and Task Force report aims to address learning objectives of the International League Against Epilepsy (ILAE) curriculum. We sought to extract detailed features involving semiology from video recordings and interpret semiological signs and symptoms that reflect the likely localization for focal seizures in patients with epilepsy. This glossary was developed by a working group of the ILAE Commission on Diagnostic Methods incorporating the EEG Task Force. This paper identifies commonly used terms to describe seizure semiology, provides definitions, signs and symptoms, and summarizes their clinical value in localizing and lateralizing focal seizures based on consensus in the published literature. Video‐EEG examples are included to illustrate important features of semiology in patients with epilepsy.

Abstract The objective of this clinical practice guideline (CPG) is to provide recommendations for healthcare personnel working with patients with epilepsy on the use of wearable devices for automated seizure detection in patients with epilepsy, in outpatient, ambulatory settings. The Working Group of the International League Against Epilepsy (ILAE) and the International Federation of Clinical Neurophysiology (IFCN) developed the CPG according to the methodology proposed by the ILAE Epilepsy Guidelines Working Group. We reviewed the published evidence using The Preferred Reporting Items for Systematic Review and Meta‐Analysis (PRISMA) statement and evaluated the evidence and formulated the recommendations following the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system. We found high level of evidence for the accuracy of automated detection of generalized tonic‐clonic seizures (GTCS) and focal‐to‐bilateral tonic‐clonic seizures (FBTCS) and recommend the use of wearable automated seizure detection devices for selected patients when accurate detection of GTCS and FBTCS is recommended as a clinical adjunct. We also found a moderate level of evidence for seizure types without GTCS or FBTCS. However, it was uncertain whether the detected alarms resulted in meaningful clinical outcomes for the patients. We recommend using clinically validated devices for automated detection of GTCS and FBTCS, especially in unsupervised patients, where alarms can result in rapid intervention (weak/conditional recommendation). At present, we do not recommend clinical use of the currently available devices for other seizure types (weak/conditional recommendation). Further research and development are needed to improve the performance of automated seizure detection and to document their accuracy and clinical utility.

AbstractEpilepsy is a heterogeneous condition with disparate etiologies and phenotypic and genotypic characteristics. Clinical and research aspects are accordingly varied, ranging from epidemiological to molecular, spanning clinical trials and outcomes, gene and drug discovery, imaging, electroencephalography, pathology, epilepsy surgery, digital technologies, and numerous others. Epilepsy data are collected in the terabytes and petabytes, pushing the limits of current capabilities. Modern computing firepower and advances in machine and deep learning, pioneered in other diseases, open up exciting possibilities for epilepsy too. However, without carefully designed approaches to acquiring, standardizing, curating, and making available such data, there is a risk of failure. Thus, careful construction of relevant ontologies, with intimate stakeholder inputs, provides the requisite scaffolding for more ambitious big data undertakings, such as an epilepsy data commons. In this review, we assess the clinical and research epilepsy landscapes in the big data arena, current challenges, and future directions, and make the case for a systematic approach to epilepsy big data.

Summary Objective Limited data are available regarding the evolution over time of the rate of sudden unexpected death in epilepsy patients (SUDEP) in drug‐resistant epilepsy. The objective is to analyze a database of 40 443 patients with epilepsy implanted with vagus nerve stimulation (VNS) therapy in the United States (from 1988 to 2012) and assess whether SUDEP rates decrease during the postimplantation follow‐up period. Methods Patient vital status was ascertained using the Centers for Disease Control and Prevention's National Death Index (NDI). An expert panel adjudicated classification of cause of deaths as SUDEP based on NDI data and available narrative descriptions of deaths. We tested the hypothesis that SUDEP rates decrease with time using the Mann‐Kendall nonparametric trend test and by comparing SUDEP rates of the first 2 years of follow‐up (years 1‐2) to longer follow‐up (years 3‐10). Results Our cohort included 277 661 person‐years of follow‐up and 3689 deaths, including 632 SUDEP. Primary analysis demonstrated a significant decrease in age‐adjusted SUDEP rate during follow‐up ( S = −27 P = .008), with rates of 2.47/1000 for years 1‐2 and 1.68/1000 for years 3‐10 (rate ratio 0.68; 95% confidence interval [CI] 0.53‐0.87; P = .002). Sensitivity analyses confirm these findings. Significance Our data suggest that SUDEP risk significantly decreases during long‐term follow‐up of patients with refractory epilepsy receiving VNS Therapy. This finding might reflect several factors, including the natural long‐term dynamic of SUDEP rate, attrition, and the impact of VNS Therapy. The role of each of these factors cannot be confirmed due to the limitations of the study.