Attention-based deep convolutional neural network for classification of generalized and focal epileptic seizures.

Epilepsy affects over 50 million people globally. Electroencephalography is critical for epilepsy diagnosis, but manual seizure classification is time-consuming and requires extensive expertise. This paper presents an automated multi-class seizure classification model using EEG signals from the Temple University Hospital Seizure Corpus ver. 1.5.2. 11 features including time-based correlation, time-based eigenvalues, power spectral density, frequency-based correlation, frequency-based eigenvalues, sample entropy, spectral entropy, logarithmic sum, standard deviation, absolute mean, and ratio of Daubechies D4 wavelet transformed coefficients were extracted from 10-second sliding windows across channels. The model combines multi-head self-attention mechanism with a deep convolutional neural network (CNN) to classify seven subtypes of generalized and focal epileptic seizures. The model achieved 0.921 weighted accuracy and 0.902 weighted F1 score in classifying focal onset non-motor, generalized onset non-motor, simple partial, complex partial, absence, tonic, and tonic-clonic seizures. In comparison, a CNN model without multi-head attention achieved 0.767 weighted accuracy. Ablation studies were conducted to validate the importance of transformer encoders and attention. The promising classification results demonstrate the potential of deep learning for handling EEG complexity and improving epilepsy diagnosis. This seizure classification model could enable timely interventions when translated into clinical practice.

Modeling seizures in the Human Phenotype Ontology according to contemporary ILAE concepts makes big phenotypic data tractable.

OBJECTIVE: The clinical features of epilepsy determine how it is defined, which in turn guides management. Therefore, consideration of the fundamental clinical entities that comprise an epilepsy is essential in the study of causes, trajectories, and treatment responses. The Human Phenotype Ontology (HPO) is used widely in clinical and research genetics for concise communication and modeling of clinical features, allowing extracted data to be harmonized using logical inference. We sought to redesign the HPO seizure subontology to improve its consistency with current epileptological concepts, supporting the use of large clinical data sets in high-throughput clinical and research genomics. METHODS: We created a new HPO seizure subontology based on the 2017 International League Against Epilepsy (ILAE) Operational Classification of Seizure Types, and integrated concepts of status epilepticus, febrile, reflex, and neonatal seizures at different levels of detail. We compared the HPO seizure subontology prior to, and following, our revision, according to the information that could be inferred about the seizures of 791 individuals from three independent cohorts: 2 previously published and 150 newly recruited individuals. Each cohort's data were provided in a different format and harmonized using the two versions of the HPO. RESULTS: The new seizure subontology increased the number of descriptive concepts for seizures 5-fold. The number of seizure descriptors that could be annotated to the cohort increased by 40% and the total amount of information about individuals' seizures increased by 38%. The most important qualitative difference was the relationship of focal to bilateral tonic-clonic seizure to generalized-onset and focal-onset seizures. SIGNIFICANCE: We have generated a detailed contemporary conceptual map for harmonization of clinical seizure data, implemented in the official 2020-12-07 HPO release and freely available at hpo.jax.org. This will help to overcome the phenotypic bottleneck in genomics, facilitate reuse of valuable data, and ultimately improve diagnostics and precision treatment of the epilepsies.

How to diagnose and classify idiopathic (genetic) generalized epilepsies.

Idiopathic or genetic generalized epilepsies (IGE) constitute an electroclinically well-defined group that accounts for almost one third of all people with epilepsy. They consist of four well-established syndromes and some other rarer phenotypes. The main four IGEs are juvenile myoclonic epilepsy, childhood absence epilepsy, juvenile absence epilepsy and IGE with generalized tonic-clonic seizures alone. There are three main seizure types in IGE, namely generalized tonic-clonic seizures, typical absences and myoclonic seizures, occurring either alone or in any combination. Diagnosing IGEs requires a multidimensional approach. The diagnostic process begins with a thorough medical history with a specific focus on seizure types, age at onset, timing and triggers. Comorbidities and family history should be questioned comprehensively. The EEG can provide valuable information for the diagnosis, including specific IGE syndromes, and therefore contribute to their optimal pharmacological treatment and management.

Genomic and Personalized Medicine Perspective in Genetic Generalized Epilepsy.

The genetic generalized epilepsies (GGEs) are a set of disorders presenting with generalized seizures, in addition to general spike-wave activity. The present study aims to investigate the clinical manifestations and genetic origin of generalized tonicclonic seizures and the subgroups of GGEs, including childhood absence epilepsy (CAE), juvenile absence epilepsy, and juvenile myoclonic epilepsy (JME). Information compiled from genome-wide association studies (GWASs) in the EPICure project revealed associations with many genes. Besides, copy number variant (CNV) discoveries have been the most inspiring turning point of epilepsy genetic research. This phenomenon could give us an idea about microdeletions/microduplications as genetic variants throughout the whole genome. Nowadays, next-generation sequencing (NGS) approaches support neurogeneticists to unravel the predisposed putative variants in GGE to establish a better diagnosis. Consequently, previous experiments supply data for antiepileptic drugs (AEDs) to test susceptible variants, which influence the response to drugs. As a final point, all these data should provide the current GGE patients with better genetic counseling and follow-up services.

Implementation of the new ILAE classification of epilepsies into clinical practice - A cohort study.

PURPOSE: Appropriate management of patients with epilepsy requires precise classification of their disease. Implementation of the recent International League Against Epilepsy (ILAE) classification of seizures and epilepsies may affect data on the relative proportions of specific types of seizures or epilepsies and should be tested in everyday practice. The aim of the study was to determine the prevalence of specific epilepsy types, syndromes, and etiologies, as defined by the new ILAE classification, in a large cohort of adult patients with epilepsy. MATERIAL AND METHODS: The single-center cohort study involved consecutive adult patients with epilepsy seen at the university epilepsy clinic. Information about medical history, neurological examination, neuroimaging, electroencephalography (EEG), genetic tests, epilepsy treatment, and other investigations was collected from medical records and prospectively updated if necessary. Epilepsy types and etiology, as well as epileptic syndromes, were classified according to the new ILAE classifications. RESULTS: We studied 653 patients (mean age: 37.2 years, 59.9% were women). Epilepsy was classified as focal in 458 cases (70.2%), generalized in 155 subjects (23.7%), or as combined focal and generalized in 11 patients (1.7%). The epilepsy type was labeled as unknown in 29 (4.4%) patients. A definite cause of epilepsy was identified in 59.4% of the cases, with a structural etiology (n = 179, 27.4%) and genetic or presumed genetic etiology (n = 169, 25.9%) being the most common. In 167 (25.5%) patients, specific epilepsy syndromes, mostly genetic generalized epilepsy syndromes, were diagnosed. CONCLUSION: The use of the recent ILAE classification of seizures and epilepsies in the cohort of patients with epilepsy seen in single epilepsy center enabled unequivocal characterization of epilepsy type in >95% of patients. A definite etiology of epilepsy could be established in about 60% of patients.

Generalized polyspike train: An EEG biomarker of drug-resistant idiopathic generalized epilepsy.

OBJECTIVE: To identify clinical and EEG biomarkers of drug resistance in adults with idiopathic generalized epilepsy. METHODS: We conducted a case-control study consisting of a discovery cohort and a replication cohort independently assessed at 2 different centers. In each center, patients with the idiopathic generalized epilepsy phenotype and generalized spike-wave discharges on EEG were classified as drug-resistant or drug-responsive. EEG changes were classified into predefined patterns and compared between the 2 groups in the discovery cohort. Factors associated with drug resistance in multivariable analysis were tested in the replication cohort. RESULTS: The discovery cohort included 85 patients (29% drug-resistant and 71% drug-responsive). Their median age at assessment was 32 years and 50.6% were female. Multivariable analysis showed that higher number of seizure types ever experienced (3 vs 1: odds ratio [OR] = 31.1, 95% confidence interval [CI]: 4.5-214, p < 0.001; 3 vs 2: OR = 14.6, 95% CI: 2.3-93.1, p = 0.004) and generalized polyspike train (burst of generalized rhythmic spikes lasting less than 1 second) during sleep were associated with drug resistance (OR = 10.8, 95% CI: 2.4-49.4, p = 0.002). When these factors were tested in the replication cohort of 80 patients (27.5% drug-resistant and 72.5% drug-responsive; 71.3% female; median age 27.5 years), the proportion of patients with generalized polyspike train during sleep was also higher in the drug-resistant group (OR = 4.0, 95% CI: 1.35-11.8, p = 0.012). CONCLUSION: Generalized polyspike train during sleep may be an EEG biomarker for drug resistance in adults with idiopathic generalized epilepsy.

Two predictors of postictal generalized EEG suppression: Tonic phase duration and postictal immobility period.

PURPOSE: To determine the effect of different seizure characteristics on the occurrence of postictal generalized EEG suppression (PGES). PGES is considered as a potential risk factor of sudden unexpected death in epilepsy (SUDEP) by several studies. METHODS: In this retrospective cross-sectional study, episodes of generalized convulsive seizures (GCS) were reviewed in regard to state at seizure-onset, the seizure and tonic phase durations, postictal immobility (PI) duration and whether the patient received oxygen (O2) mask during the post-ictal phase. Moreover, the presence and duration of PGES was determined for each seizure. RESULTS: Among 98 episodes of GCSs, 56 (57.1%) had PGES and 42 (42.9%) did not have PGES. The mean seizure duration for attacks with and without PGES was 106.62 ± 97.04 and 104.85 ± 91.81 s, respectively (P > 0.05). The tonic phase duration was significantly longer in PGES positive compared to PGES negative seizures (4.25 ± 3.17 s vs. 2.82 ± 3.58 s, P < 0.05). Early O2 mask administration and state of wakefulness at seizure-onset did not show any significant correlation with the presence of PGES (P > 0.05). Seizures with PGES had higher PI duration than those without PGES (156.24 s vs. 124.73 s) (P < 0.05). Interestingly, in seizures with PGES, there was a positive correlation between PI and tonic phase durations (r: 0.4, P < 0.05). CONCLUSIONS: According to our findings, higher tonic phase duration and longer PI period increased the odds of PGES formation.

Crossing the lines between epilepsy syndromes: a myoclonic epilepsy variant with prominent eyelid myoclonia and atonic components.

Accurate diagnosis of a distinct epilepsy syndrome is based on well-defined electroclinical features that differentiate separate nosological entities. In clinical practice, however, syndromes may overlap and cases may present with unusual manifestations posing a diagnostic challenge. This heterogeneity has been documented in several cases presenting with eyelid myoclonia with or without absences (EMA) diagnosed either as Jeavons syndrome (JS) variants or as genetic generalised epilepsies defined by the presence of this unique clinical entity. The hallmark of JS is the triad: (1) eyelid myoclonia with or without absences, (2) eye closure-induced paroxysms, and (3) photosensitivity. The presence of massive myoclonus, intellectual disability, or slowing of the EEG background are not typical features of the syndrome and may cause delay in making the correct diagnosis. Adding to the variability of clinical features, we describe two female paediatric patients with probable genetic epilepsy who presented with EMA but demonstrated clear atypical features, such as prominent myoclonic seizures, atonic components on video-EEG, and cognitive impairment. We also note the presence of interictal and ictal posterior discharges during eyelid myoclonia in one, supporting similar previous observations leading to consideration of EMA as an occipital cortex-initiated seizure activity. [Published with video sequences on www.epilepticdisorders.com].

Effective clinical classification of chronic epilepsy into focal and generalized: A cross sectional study.

PURPOSE: Investigations such as EEG and brain imaging are often difficult to obtain in primary care settings of resource-limited regions impacting millions of epilepsy patients. We wanted to test the hypothesis that classification of chronic epilepsy into focal and generalized based on clinical history and examination alone would be comparable to making such a classification with additional inputs from EEG and brain imaging. METHODS: Two investigators independently classified consecutive chronic epilepsy patients into focal, generalized and unclassified epilepsy. Investigator 1 made this determination using clinical history and examination alone whereas Investigator II additionally used EEG and brain imaging too. We calculated inter observer agreement between the two investigators and also looked at the predictors of focal and generalized epilepsy. RESULTS: Five hundred and twelve patients were recruited. Inter observer agreement between the two investigators in making the focal versus generalized classification was 96.8%, kappa 0.91 (p<0.0001). When EEG and neuroimaging findings were added to clinical information, there was a change in classification in 3.2% patients. Several predictors of focal and generalized epilepsy were identified. CONCLUSIONS: Classification of chronic epilepsy into focal and generalized can be done reliably in most patients using clinical information alone. Investigating chronic epilepsy patients with EEG and brain imaging may not be necessary in every patient. The results of our study are especially significant for epilepsy patients living in resource-limited regions where such investigations may not always be available.

Phenotypic analysis of 303 multiplex families with common epilepsies.

Gene identification in epilepsy has mainly been limited to large families segregating genes of major effect and de novo mutations in epileptic encephalopathies. Many families that present with common non-acquired focal epilepsies and genetic generalized epilepsies remain unexplained. We assembled a cohort of 'genetically enriched' common epilepsies by collecting and phenotyping families containing multiple individuals with unprovoked seizures. We aimed to determine if specific clinical epilepsy features aggregate within families, and whether this segregation of phenotypes may constitute distinct 'familial syndromes' that could inform genomic analyses. Families with three or more individuals with unprovoked seizures were studied across multiple international centres. Affected individuals were phenotyped and classified according to specific electroclinical syndromes. Families were categorized based on syndromic groupings of affected family members, examined for pedigree structure and phenotypic patterns and, where possible, assigned specific familial epilepsy syndromes. A total of 303 families were assembled and analysed, comprising 1120 affected phenotyped individuals. Of the 303 families, 117 exclusively segregated generalized epilepsy, 62 focal epilepsy, and 22 were classified as genetic epilepsy with febrile seizures plus. Over one-third (102 families) were observed to have mixed epilepsy phenotypes: 78 had both generalized and focal epilepsy features within the same individual (n = 39), or within first or second degree relatives (n = 39). Among the genetic generalized epilepsy families, absence epilepsies were found to cluster within families independently of juvenile myoclonic epilepsy, and significantly more females were affected than males. Of the 62 familial focal epilepsy families, two previously undescribed familial focal syndrome patterns were evident: 15 families had posterior quadrant epilepsies, including seven with occipito-temporal localization and seven with temporo-parietal foci, and four families displayed familial focal epilepsy of childhood with multiple affected siblings that was suggestive of recessive inheritance. The findings suggest (i) specific patterns of syndromic familial aggregation occur, including newly recognized forms of familial focal epilepsy; (ii) although syndrome-specificity usually occurs in multiplex families, the one-third of families with features of both focal and generalized epilepsy is suggestive of shared genetic determinants; and (iii) patterns of features observed across families including pedigree structure, sex, and age of onset may hold clues for future gene identification. Such detailed phenotypic information will be invaluable in the conditioning and interpretation of forthcoming sequencing data to understand the genetic architecture and inter-relationships of the common epilepsy syndromes.

[Classification of idiopathic generalised epilepsies in patients over 16 years of age]. / Clasificacion de las epilepsias generalizadas idiopaticas en mayores de 16 años.

INTRODUCTION: Idiopathic generalised epilepsies (IGE) are a set of electroclinical syndromes with different phenotypes. Our aim is to analyse those phenotypes in patients over 16 years of age. PATIENTS AND METHODS: We conducted a retrospective analysis of a series of patients with IGE. They were classified as childhood absence epilepsy (CAE), juvenile absence epilepsy (JAE), juvenile myoclonic epilepsy (JME), epilepsy with tonic-clonic seizures only (TCSE), epilepsy with eyelid myoclonias and absences (EMA) and pure photogenic epilepsy (PE). RESULTS: We included 308 patients, the majority females (56.8%), in our study. JME was the most prevalent (40.9%), followed by TCSE (30%), JAE (10%), EMA (8.7%), CAE (7.7%) and PE (1.6%). The types of seizures presented by the most patients were tonic-clonic (89.6%), myoclonic (45.4%), absence (31.4%), reflex seizures (13.3%), eyelid myoclonias (12.6%), non-epileptic psychogenic seizures (3.6%) and status epilepticus (1.9%). They all had generalised spike-and-wave discharges in the electroencephalogram (EEG). 19.2% presented asymmetrical discharges and 28.2% showed a photoparoxysmal response. We observed differences between syndromes in polytherapy (p < 0.0001), withdrawal of therapy (p = 0.01) and being seizure-free beyond the age of 50 (p = 0.004). CONCLUSIONS: JME was the most frequent. Generalised tonic-clonic seizures were the type of seizures presented by the most patients, followed by myoclonic, absent and reflex seizures. The EEG showed a photoparoxysmal response in over a quarter of the patients, and one in five displayed asymmetrical anomalies. Differences were observed according to the syndrome in polytherapy, persistence of seizures and withdrawal of treatment.

TITLE: Clasificacion de las epilepsias generalizadas idiopaticas en mayores de 16 años.Introduccion. Las epilepsias generalizadas idiopaticas (EGI) son un conjunto de sindromes electroclinicos con distintos fenotipos. Nuestro objetivo es analizar dichos fenotipos en pacientes mayores de 16 años. Pacientes y metodos. Analizamos retrospectivamente una serie de pacientes con EGI. Los clasificamos en epilepsia de ausencias infantil (EAI), epilepsia de ausencias juvenil (EAJ), epilepsia mioclonica juvenil (EMJ), epilepsia con crisis tonicoclonicas solo (ECTC), epilepsia con ausencias y mioclonias palpebrales (EAM) y epilepsia fotogenica pura (EF). Resultados. Incluimos 308 pacientes, mayoritariamente mujeres (56,8%). La EMJ fue mas prevalente (40,9%), seguida de la ECTC (30%), la EAJ (10%), la EAM (8,7%), la EAI (7,7%) y la EF (1,6%). Los tipos de crisis que presentaron mas pacientes fueron las tonicoclonicas (89,6%), las mioclonicas (45,4%), las ausencias (31,4%), las crisis reflejas (13,3%), las mioclonias palpebrales (12,6%), las crisis psicogenas no epilepticas (3,6%) y el estado epileptico (1,9%). Todos tenian descargas punta-onda generalizada en el electroencefalograma (EEG). El 19,2% presento descargas asimetricas y el 28,2%, respuesta fotoparoxistica. Observamos diferencias entre sindromes en politerapia (p < 0,0001), retirada de tratamiento (p = 0,01) y estar libres de crisis por encima de los 50 años (p = 0,004). Conclusiones. La EMJ fue la EGI mas frecuente. Las crisis tonicoclonicas generalizadas fueron el tipo de crisis que presentaron mas pacientes, seguidas de las mioclonicas, las ausencias y las crisis reflejas. El EEG mostro en mas de una cuarta parte de los pacientes una respuesta fotoparoxistica, y en uno de cada cinco, anomalias asimetricas. Se observaron diferencias segun el sindrome en politerapia, persistencia de crisis y retirada de tratamiento.

Clasificación de las epilepsias generalizadas idiopáticas en mayores de 16 años / Classification of idiopathic generalised epilepsies in patients over 16 years of age

Introducción. Las epilepsias generalizadas idiopáticas (EGI) son un conjunto de síndromes electroclínicos con distintos fenotipos. Nuestro objetivo es analizar dichos fenotipos en pacientes mayores de 16 años. Pacientes y métodos. Analizamos retrospectivamente una serie de pacientes con EGI. Los clasificamos en epilepsia de ausencias infantil (EAI), epilepsia de ausencias juvenil (EAJ), epilepsia mioclónica juvenil (EMJ), epilepsia con crisis tonicoclónicas sólo (ECTC), epilepsia con ausencias y mioclonías palpebrales (EAM) y epilepsia fotogénica pura (EF). Resultados. Incluimos 308 pacientes, mayoritariamente mujeres (56,8%). La EMJ fue más prevalente (40,9%), seguida de la ECTC (30%), la EAJ (10%), la EAM (8,7%), la EAI (7,7%) y la EF (1,6%). Los tipos de crisis que presentaron más pacientes fueron las tonicoclónicas (89,6%), las mioclónicas (45,4%), las ausencias (31,4%), las crisis reflejas (13,3%), las mioclonías palpebrales (12,6%), las crisis psicógenas no epilépticas (3,6%) y el estado epiléptico (1,9%). Todos tenían descargas punta-onda generalizada en el electroencefalograma (EEG). El 19,2% presentó descargas asimétricas y el 28,2%, respuesta fotoparoxística. Observamos diferencias entre síndromes en politerapia (p < 0,0001), retirada de tratamiento (p = 0,01) y estar libres de crisis por encima de los 50 años (p = 0,004). Conclusiones. La EMJ fue la EGI más frecuente. Las crisis tonicoclónicas generalizadas fueron el tipo de crisis que presentaron más pacientes, seguidas de las mioclónicas, las ausencias y las crisis reflejas. El EEG mostró en más de una cuarta parte de los pacientes una respuesta fotoparoxística, y en uno de cada cinco, anomalías asimétricas. Se observaron diferencias según el síndrome en politerapia, persistencia de crisis y retirada de tratamiento (AU)

Introduction. Idiopathic generalised epilepsies (IGE) are a set of electroclinical syndromes with different phenotypes. Our aim is to analyse those phenotypes in patients over 16 years of age. Patients and methods. We conducted a retrospective analysis of a series of patients with IGE. They were classified as childhood absence epilepsy (CAE), juvenile absence epilepsy (JAE), juvenile myoclonic epilepsy (JME), epilepsy with tonicclonic seizures only (TCSE), epilepsy with eyelid myoclonias and absences (EMA) and pure photogenic epilepsy (PE). Results. We included 308 patients, the majority females (56.8%), in our study. JME was the most prevalent (40.9%), followed by TCSE (30%), JAE (10%), EMA (8.7%), CAE (7.7%) and PE (1.6%). The types of seizures presented by the most patients were tonic-clonic (89.6%), myoclonic (45.4%), absence (31.4%), reflex seizures (13.3%), eyelid myoclonias (12.6%), non-epileptic psychogenic seizures (3.6%) and status epilepticus (1.9%). They all had generalised spike-and-wave discharges in the electroencephalogram (EEG). 19.2% presented asymmetrical discharges and 28.2% showed a photoparoxysmal response. We observed differences between syndromes in polytherapy (p < 0.0001), withdrawal of therapy (p = 0.01) and being seizure-free beyond the age of 50 (p = 0.004). Conclusions. JME was the most frequent. Generalised tonic-clonic seizures were the type of seizures presented by the most patients, followed by myoclonic, absent and reflex seizures. The EEG showed a photoparoxysmal response in over a quarter of the patients, and one in five displayed asymmetrical anomalies. Differences were observed according to the syndrome in polytherapy, persistence of seizures and withdrawal of treatment (AU)

Data fusion for paroxysmal events' classification from EEG.

BACKGROUND: Spatiotemporal analysis of electroencephalography is commonly used for classification of events since it allows capturing dependencies across channels. The significant increase of feature vector dimensionality however introduce noise and thus it does not allow the classification models to be trained using a limited number of samples usually available in clinical studies. NEW METHOD: Thus, we investigate the classification of epileptic and non-epileptic events based on temporal and spectral analysis through the application of three different fusion schemes for the combination of information across channels. We compare the commonly used early-integration (EI) scheme - in which features are fused from all channels prior to classification - with two late-integration (LI) schemes performing per channel classification when: (i) the temporal context varies significantly across channels, thus local spatial training models are required, and (ii) the spatial variations are negligible in comparison to the inter-subject variation, thus only the temporal variation is modeled using a single global spatial training model. Furthermore, we perform dimensionality reduction either by feature selection or by principal component analysis. RESULTS: The framework is applied on events that manifest across most channels, as generalized epileptic seizures, psychogenic non-epileptic seizures and vasovagal syncope. The three classification architectures were evaluated on EEG epochs from 11 subjects. COMPARISON WITH EXISTING METHODS: Although direct comparison with other studies is difficult due to the different characteristics of each dataset, the achieved recognition accuracy of the LI fusion schemes outperforms the performance reported in the literature. CONCLUSIONS: The best scheme was the LI with global model which achieved 97% accuracy.

Circadian phase typing in idiopathic generalized epilepsy: Dim light melatonin onset and patterns of melatonin secretion-Semicurve findings in adult patients.

OBJECTIVE/BACKGROUND: It has been debated in the literature whether patients with idiopathic generalized epilepsy (IGE) have a distinctive, evening-oriented chronotype. The few questionnaire-based studies that are available in the literature have conflicting results. The aim of our study was to define chronotype in patients with IGE by determining dim light melatonin onset (DLMO). PATIENTS/METHODS: Twenty adults diagnosed with IGE (grand mal on awakening [GM] in 7 cases and juvenile myoclonic epilepsy in 13 cases) were investigated by means of a face-to-face semistructured sleep interview, Morningness-Eveningness Questionnaire (MEQ), Pittsburgh Sleep Quality Index (PSQI) questionnaire, and a melatonin salivary test with DLMO determination. Eighteen healthy subjects (HC) and 28 patients affected with cryptogenic focal epilepsy (FE) served as controls. RESULTS: The mean MEQ score was significantly lower in patients with IGE than that in patients with FE (49.1±5.9 versus 56.1±8.7 P<0.01) but not significantly lower than that in HC (49.1±5.9 versus 49.3±8.6). Midsleep on free days corrected for sleep duration did not differ significantly between the three subject groups (04:59±01:21h, 04:37±01:17h, 04:29±00:52h). The mean DLMO time in patients with IGE (22:13±01:34h) occurred 49min later than that in HC (21.24±1h), and the melatonin surge within the 30-minute time interval after DLMO in patients with IGE was significantly lower than that in HC (1.51±2.7 versus 3.8±3.6pg/mL P=0.045). CONCLUSIONS: Subjective measures of chronotype do not indicate a definite evening-oriented chronotype in patients with IGE. However, the data concerning endogenous melatonin secretion indicate that patients with IGE tend to have a late circadian phase. Further studies are warranted in order to better define the late pattern of endogenous melatonin secretion in patients with IGE and to ascertain the role of this pattern in influencing behavioral chronotype in these subjects.

Risk factors of postictal generalized EEG suppression in generalized convulsive seizures.

OBJECTIVE: To identify the clinical determinants of occurrence of postictal generalized EEG suppression (PGES) after generalized convulsive seizures (GCS). METHODS: We reviewed the video-EEG recordings of 417 patients included in the REPO2MSE study, a multicenter prospective cohort study of patients with drug-resistant focal epilepsy. According to ictal semiology, we classified GCS into 3 types: tonic-clonic GCS with bilateral and symmetric tonic arm extension (type 1), clonic GCS without tonic arm extension or flexion (type 2), and GCS with unilateral or asymmetric tonic arm extension or flexion (type 3). Association between PGES and person-specific or seizure-specific variables was analyzed after correction for individual effects and the varying number of seizures. RESULTS: A total of 99 GCS in 69 patients were included. Occurrence of PGES was independently associated with GCS type (p < 0.001) and lack of early administration of oxygen (p < 0.001). Odds ratio (OR) for GCS type 1 in comparison with GCS type 2 was 66.0 (95% confidence interval [CI 5.4-801.6]). In GCS type 1, risk of PGES was significantly increased when the seizure occurred during sleep (OR 5.0, 95% CI 1.2-20.9) and when oxygen was not administered early (OR 13.4, 95% CI 3.2-55.9). CONCLUSION: The risk of PGES dramatically varied as a function of GCS semiologic characteristics. Whatever the type of GCS, occurrence of PGES was prevented by early administration of oxygen.

Common variants of KCNJ10 are associated with susceptibility and anti-epileptic drug resistance in Chinese genetic generalized epilepsies.

To explore genetic mechanism of genetic generalized epilepsies (GGEs) is challenging because of their complex heritance pattern and genetic heterogeneity. KCNJ10 gene encodes Kir4.1 channels and plays a major role in modulating resting membrane potentials in excitable cells. It may cause GGEs if mutated. The purpose of this study was to investigate the possible association between KCNJ10 common variants and the susceptibility and drug resistance of GGEs in Chinese population. The allele-specific MALDI-TOF mass spectrometry method was used to assess 8 single nucleotide polymorphisms (SNPs) of KCNJ10 in 284 healthy controls and 483 Chinese GGEs patients including 279 anti-epileptic drug responsive patients and 204 drug resistant patients. We found the rs6690889 TC+TT genotypes were lower frequency in the GGEs group than that in the healthy controls (6.7% vs 9.5%, p = 0.01, OR = 0.50[0.29-0.86]). The frequency of rs1053074 G allele was lower in the childhood absence epilepsy (CAE) group than that in the healthy controls (28.4% vs 36.2%, p = 0.01, OR = 0.70[0.53-0.93]). The frequency of rs12729701 G allele and AG+GG genotypes was lower in the CAE group than that in the healthy controls (21.2% vs 28.4%, p = 0.01, OR = 0.74[0.59-0.94] and 36.3% vs 48.1%, p = 0.01, OR = 0.83[0.72-0.96], respectively). The frequency of rs12402969 C allele and the CC+CT genotypes were higher in the GGEs drug responsive patients than that in the drug resistant patients (9.3% vs 5.6%, OR = 1.73[1.06-2.85], p = 0.026 and 36.3% vs 48.1%, p = 0.01, OR = 0.83[0.72-0.96], respectively). This study identifies potential SNPs of KCNJ10 gene that may contribute to seizure susceptibility and anti-epileptic drug resistance.

Understanding ictogenesis in generalized epilepsies.

Generalized seizures are defined by bilateral symmetric and synchronous epileptiform EEG discharge over the entire convexity and commonly thought to involve the entire brain homogeneously. The characteristic 3-3.5 Hz Spike-and-Wave pattern is conceived as a resonance phenomenon originating in a cortico-thalamic circuit where it can start at variable sites. Investigations with EEG source analysis, magnetencephalography, positron emission tomography and single photon emission computerized tomography, functional magnetic resonance imaging and transcranial magnetic stimulation have suggested that generalized seizures have cortical onset and the thalamus has an essential role in the recruitment of a network comprising frontal, parietal and occipital cortex and the default mode network. Studies of reflex epileptic traits have shown that 'generalized' ictogenesis largely uses pre-existing functional anatomic networks normally serving physiological functions. It has therefore been proposed to consider these epilepsies as system disorders of the brain. Treatment is fundamentally pharmacological with a role for behavioral interventions. Generalized epileptic encephalopathies of early childhood are sometimes surgically remediable.