The role of EEG in the diagnosis and classification of the epilepsy syndromes: a tool for clinical practice by the ILAE Neurophysiology Task Force (Part 1)

Abstract

Rational. The concept of epilepsy syndromes, introduced in 1989, was defined as "clusters of signs and symptoms customarily occurring together". Definition of epilepsy syndromes based on electro-clinical features facilitated clinical practice and, whenever possible, clinical research in homogeneous groups of patients with epilepsies. Progress in the fields of neuroimaging and genetics made it rapidly clear that, although crucial, * M. Koutroumanidis, et al. the electro-clinical description of epilepsy syndromes was not sufficient to allow much needed development of targeted therapies and a better understanding of the underlying pathophysiological mechanisms of seizures. The 2017 ILAE position paper on Classification of the Epilepsies recognized that "as a critical tool for the practicing clinician, epilepsy classification must be relevant and dynamic to changes in thinking". The concept of "epilepsy syndromes" evolved, incorporating issues related to aetiologies and comorbidities. A comprehensive update (and revision where necessary) of the EEG diagnostic criteria in the light of the 2017 revised terminology and concepts was deemed necessary. Methods. The work was commissioned by the Neurophysiology Task Force of the ILAE Committee on the Diagnostic Methods. Diagnostic criteria and recording procedures were developed by group consensus, reached through an "informal", internal decision-making process. Each working group member was allocated a number of syndromes, and a standard structured template was used. International literature was extensively reviewed. Results. We developed a simple diagnostic system that is applicable to all epilepsy syndromes which allows the physician (i) to rate the strength of EEG diagnosis (degree of diagnostic certainty) by weighting EEG findings in relation to the available clinical information or the specific clinical question, and ii) to suggest further EEG diagnostics where conclusive diagnostic evidence is lacking. We also propose a system of syndrome-specific recording protocols that, used with the relevant clinical presentation or specific clinical question, may maximize activation of epileptic discharges and ultimately help with standardization of EEG recording across departments, worldwide. Because recording methodology also depends on available resources, a two-tier system was developed to embrace clinical EEG services in resourcelimited and industrialized countries. A clinical practice statement for each of the epilepsy syndromes discussed underscores the crucial role of the clinical information with regards to both the optimization of the EEG recording and mainly its meaningful interpretation. Part I covers Genetic (Idiopathic) generalized epilepsies and syndromes, Reflex epilepsies, structural and genetic focal (lobar) syndromes and Progressive Myoclonus Epilepsies [Published with educational EEG plates on www.epilepticdisorders.com].

An automated, machine learning–based detection algorithm for spike‐wave discharges (SWDs) in a mouse model of absence epilepsy

“…Using the output of the algorithm, we calculated the event-triggered average: (ie, the average of all segments of one signal aligned to the time of events in another signal) of 50 random examples of strong SWDs (distance to support vector of >1 to 2), moderate SWDs (>0.5 to 1), weak SWDs (>0 to 0.5), weak nonSWDs (<0 to −0.5), moderate nonSWDs (<−0.5 to −1), and strong non-SWDs (<−1 to −2), and 50 random locations not identified by our algorithm to delta (0.5-4 Hz), theta (6-9 Hz), sigma (10)(11)(12)(13)(14), and gamma (25-100 Hz) power surrounding each event during lights-on and lights-off periods. Using the output of the algorithm, we calculated the event-triggered average: (ie, the average of all segments of one signal aligned to the time of events in another signal) of 50 random examples of strong SWDs (distance to support vector of >1 to 2), moderate SWDs (>0.5 to 1), weak SWDs (>0 to 0.5), weak nonSWDs (<0 to −0.5), moderate nonSWDs (<−0.5 to −1), and strong non-SWDs (<−1 to −2), and 50 random locations not identified by our algorithm to delta (0.5-4 Hz), theta (6-9 Hz), sigma (10)(11)(12)(13)(14), and gamma (25-100 Hz) power surrounding each event during lights-on and lights-off periods.…”

“…3 Such inconsistencies are not well characterized for SWD detection (we present a detailed example here); however, inconsistencies have been described in the scoring of other nonconvulsive epileptiform events (eg, interictal spikes). First, the definition of "epileptiform" is often vague [10][11][12] and is subject to disagreement. First, the definition of "epileptiform" is often vague [10][11][12] and is subject to disagreement.…”

“…9 Indeed, nonconvulsive epileptiform events are difficult to identify and classify for numerous reasons. First, the definition of "epileptiform" is often vague [10][11][12] and is subject to disagreement. Clinicians and researchers typically use subjective criteria, such as that an event "stands out of the background," 13 to force events into binary categories.…”

The 2017 ILAE classification of seizure types and the epilepsies: what do people with epilepsy and their caregivers need to know?

“…The next group consists of people who have both focal and generalized seizures. The VEEG (Koutroumanidis et al, 2017b) can be helpful in defining this category. In the severe epilepsies of infancy and childhood, the EEG is often markedly abnormal.…”

An automated, machine learning-based detection algorithm for spike-wave discharges (SWDs) in a mouse model of absence epilepsy

“…Indeed, nonconvulsive epileptiform events are difficult to identify and classify for numerous reasons. First, the definition of 'epileptiform' is often vague [10][11][12] and is subject to disagreement. Clinicians and researchers typically use subjective criteria, such as that an event 'stands out of the background' 13 , to force events into binary categories.…”