Focal electroclinical features in generalized tonic-clonic seizures: Decision flowchart for a diagnostic challenge.

OBJECTIVE: Bilateral tonic-clonic seizures with focal semiology or focal interictal electroencephalography (EEG) can occur in both focal and generalized epilepsy types, leading to diagnostic errors and inappropriate therapy. We investigated the prevalence and prognostic values of focal features in patients with idiopathic generalized epilepsy (IGE), and we propose a decision flowchart to distinguish between focal and generalized epilepsy in patients with bilateral tonic-clonic seizures and focal EEG or semiology. METHODS: We retrospectively analyzed video-EEG recordings of 101 bilateral tonic-clonic seizures from 60 patients (18 with IGE, 42 with focal epilepsy). Diagnosis and therapeutic response were extracted after ≥1-year follow-up. The decision flowchart was based on previous observations and assessed concordance between interictal and ictal EEG. RESULTS: Focal semiology in IGE was observed in 75% of seizures and 77.8% of patients, most often corresponding to forced head version (66.7%). In patients with multiple seizures, direction of head version was consistent across seizures. Focal interictal epileptiform discharges (IEDs) were observed in 61.1% of patients with IGE, whereas focal ictal EEG onset only occurred in 13% of seizures and 16.7% of patients. However, later during the seizures, a reproducible pattern of 7-Hz lateralized ictal rhythm was observed in 56% of seizures, associated with contralateral head version. We did not find correlation between presence of focal features and therapeutic response in IGE patients. Our decision flowchart distinguished between focal and generalized epilepsy in patients with bilateral tonic-clonic seizures and focal features with an accuracy of 96.6%. SIGNIFICANCE: Focal semiology associated with bilateral tonic-clonic seizures and focal IEDs are common features in patients with IGE, but focal ictal EEG onset is rare. None of these focal findings appears to influence therapeutic response. By assessing the concordance between interictal and ictal EEG findings, one can accurately distinguish between focal and generalized epilepsies.

Reliable detection of generalized convulsive seizures using an off-the-shelf digital watch: A multisite phase 2 study.

OBJECTIVE: The aim of this study was to develop a machine learning algorithm using an off-the-shelf digital watch, the Samsung watch (SM-R800), and evaluate its effectiveness for the detection of generalized convulsive seizures (GCS) in persons with epilepsy. METHODS: This multisite epilepsy monitoring unit (EMU) phase 2 study included 36 adult patients. Each patient wore a Samsung watch that contained accelerometer, gyroscope, and photoplethysmographic sensors. Sixty-eight time and frequency domain features were extracted from the sensor data and were used to train a random forest algorithm. A testing framework was developed that would better reflect the EMU setting, consisting of (1) leave-one-patient-out cross-validation (LOPO CV) on GCS patients, (2) false alarm rate (FAR) testing on nonseizure patients, and (3) "fixed-and-frozen" prospective testing on a prospective patient cohort. Balanced accuracy, precision, sensitivity, and FAR were used to quantify the performance of the algorithm. Seizure onsets and offsets were determined by using video-electroencephalographic (EEG) monitoring. Feature importance was calculated as the mean decrease in Gini impurity during the LOPO CV testing. RESULTS: LOPO CV results showed balanced accuracy of .93 (95% confidence interval [CI] = .8-.98), precision of .68 (95% CI = .46-.85), sensitivity of .87 (95% CI = .62-.96), and FAR of .21/24 h (interquartile range [IQR] = 0-.90). Testing the algorithm on patients without seizure resulted in an FAR of .28/24 h (IQR = 0-.61). During the "fixed-and-frozen" prospective testing, two patients had three GCS, which were detected by the algorithm, while generating an FAR of .25/24 h (IQR = 0-.89). Feature importance showed that heart rate-based features outperformed accelerometer/gyroscope-based features. SIGNIFICANCE: Commercially available wearable digital watches that reliably detect GCS, with minimum false alarm rates, may overcome usage adoption and other limitations of custom-built devices. Contingent on the outcomes of a prospective phase 3 study, such devices have the potential to provide non-EEG-based seizure surveillance and forecasting in the clinical setting.

Epilepsy with generalized tonic-clonic seizures alone: Electroclinical features and prognostic patterns.

OBJECTIVE: Epilepsy with generalized tonic-clonic seizures alone (GTCA) is a common but poorly characterized idiopathic generalized epilepsy (IGE) syndrome. Hence, we investigated electroclinical features, seizure outcome, and antiseizure medication (ASM) withdrawal in a large cohort of GTCA patients. METHODS: In this multicenter retrospective study, GTCA patients defined according to the diagnostic criteria of the International League Against Epilepsy (2022) were included. We investigated prognostic patterns, drug resistance at the last visit, and ASM withdrawal, along with their prognostic factors. RESULTS: We included 247 patients with a median (interquartile range [IQR]) age at onset of 17 years (13-22) and a median follow-up duration of 10 years (IQR = 5-20). Drug resistance at the last visit was observed in 40 (16.3%) patients, whereas the median latency to achieve 2-year remission was 24 months (IQR = 24-46.5) with a median number of 1 (IQR = 1-2) ASM. During the long-term follow-up (i.e., 202 patients followed ≥5-years after the first ASM trial), 69 (34.3%) patients displayed an early remission pattern and 36 (17.9%) patients displayed a late remission pattern, whereas 16 (8%) and 73 (36.3%) individuals had no-remission and relapsing-remitting patterns, respectively. Catamenial seizures and morning predominance of generalized tonic-clonic seizures (GTCS) independently predicted drug resistance at the last visit according to multivariable logistic regression. Treatment withdrawal was attempted in 63 (25.5%) patients, with 59 (93.7%) of them having at least a 12-month follow-up after ASM discontinuation. At the last visit, 49 (83%) of those patients had experienced GTCS recurrence. A longer duration of seizure freedom was the only factor predicting a higher chance of successful ASM withdrawal according to multivariable Cox regression. SIGNIFICANCE: GTCA could be considered a relatively easily manageable IGE syndrome, with a low rate of drug resistance and a high prevalence of early response to treatment. Nevertheless, a considerable proportion of patients experience relapsing patterns of seizure control, highlighting the need for appropriate counseling and lifestyle recommendations.

EEG in focal and generalized epilepsies: Pearls and perils.

Correctly diagnosing and classifying seizures and epilepsies is vital to ensure a tailored approach to patients with epilepsy. The ILAE seizure classification consists of two main groups: focal and generalized. Establishing if a seizure is focal or generalized is essential to classify the epilepsy type and the epilepsy syndrome, providing more personalized treatment and counseling about prognosis. EEG is one of the most essential tools for this classification process and further localization of the epileptogenic focus. However, some EEG findings are misleading and may postpone the correct diagnosis and proper treatment. Knowing the most common EEG pitfalls in focal and generalized epilepsies is valuable for clinical practice, avoiding misinterpretations. Some atypical features can be challenging in focal epilepsies, such as secondary bilateral synchrony, focal epileptiform activity induced by hyperventilation and photic stimulation, and non-focal slowing. On the other hand, more than 60 % of persons with idiopathic generalized epilepsies have at least one type of atypical abnormality. In this manuscript, we describe and illustrate some of the most common EEG findings that can make even experienced epileptologists question not only where the epileptogenic focus is but also if the patient has focal or generalized epilepsy. This review summarizes the perils and provide some pearls to assist EEG readers.

Coexistence of temporal lobe epilepsy and idiopathic generalized epilepsy.

OBJECTIVE: We investigated the frequency of coexistence of temporal lobe epilepsy (TLE) and idiopathic generalized epilepsy (IGE) in a retrospective database study. We also explored the underlying pathomechanisms of the coexistence of TLE and IGE based on the available information, using bioinformatics tools. METHODS: The first phase of the investigation was a retrospective study. All patients with an electro-clinical diagnosis of epilepsy were studied at the outpatient epilepsy clinic at Shiraz University of Medical Sciences, Shiraz, Iran, from 2008 until 2023. In the second phase, we searched the following databases for genetic variations (epilepsy-associated genetic polymorphisms) that are associated with TLE or syndromes of IGE: DisGeNET, genome-wide association study (GWAS) Catalog, epilepsy genetic association database (epiGAD), and UniProt. We also did a separate literature search using PubMed. RESULTS: In total, 3760 patients with epilepsy were registered at our clinic; four patients with definitely mixed TLE and IGE were identified; 0.1% of all epilepsies. We could identify that rs1883415 of ALDH5A1, rs137852779 of EFHC1, rs211037 of GABRG2, rs1130183 of KCNJ10, and rs1045642 of ABCB1 genes are shared between TLE and syndromes of IGE. CONCLUSION: While coexistence of TLE and IGE is a rare phenomenon, this could be explained by shared genetic variations.

Construction and validation of an algorithm to separate focal and generalised epilepsy using clinical variables: A comparison of machine learning approaches.

PURPOSE: Epilepsy type, whether focal or generalised, is important in deciding anti-seizure medication (ASM). In resource-limited settings, investigations are usually not available, so a clinical separation is required. We used a naïve Bayes approach to devise an algorithm to do this, and compared its accuracy with algorithms devised by five other machine learning methods. METHODS: We used data on 28 clinical variables from 503 patients attending an epilepsy clinic in India with defined epilepsy type, as determined by an epileptologist with access to clinical, imaging, and EEG data. We adopted a machine learning approach to select the most relevant variables based on mutual information, to train the model on part of the data, and then to evaluate it on the remaining data (testing set). We used a naïve Bayes approach and compared the results in the testing set with those obtained by several other machine learning algorithms by measuring sensitivity, specificity, accuracy, area under the curve, and Cohen's kappa. RESULTS: The six machine learning methods produced broadly similar results. The best naïve Bayes algorithm contained eleven variables, and its accuracy was 92.2% in determining epilepsy type (sensitivity 92.0%, specificity 92.7%). An algorithm incorporating the best eight of these variables was only slightly less accurate - 91.0% (sensitivity 89.6%, and specificity 95.1%) - and easier for clinicians to use. CONCLUSION: A clinical algorithm with eight variables is effective and accurate at separating focal from generalised epilepsy. It should be useful in resource-limited settings, by epilepsy-inexperienced doctors, to help determine epilepsy type and therefore optimal ASMs for individual patients, without the need for EEG or neuroimaging.

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.

Drug-resistant generalized epilepsies: Revisiting the frontiers of idiopathic generalized epilepsies.

The 2017 International League Against Epilepsy (ILAE) classification suggested that the term "genetic generalized epilepsies" (GGEs) should be used for the broad group of epilepsies with so-called "generalized" seizure types and "generalized" spike-wave activity on EEG, based on a presumed genetic etiology. Within this framework, idiopathic generalized epilepsies (IGEs) are described as a subset of GGEs and include only four epileptic syndromes: childhood absence epilepsy, juvenile absence epilepsy, juvenile myoclonic epilepsy, and epilepsy with generalized tonic-clonic seizures alone. The recent 2022 ILAE definition of IGEs is based on the current state of knowledge and reflects a community consensus and is designed to evolve as knowledge advances. The term "frontiers of IGEs" refers to the actual limits of our understanding of these four syndromes. Indeed, among patients presenting with a syndrome compatible with the 2022 definition of IGEs, we still observe a significant proportion of patients presenting with specific clinical features, refractory seizures, or drug-resistant epilepsies. This leads to the discussion of the boundaries of IGEs and GGEs, or what is accepted within a clinical spectrum of a definite IGE. Here, we discuss several entities that have been described in the literature for many years and that may either constitute rare features of IGEs or a distinct differential diagnosis. Their recognition by clinicians may allow a more individualized approach and improve the management of patients presenting with such entities.

Clinical presentation and evaluation of epilepsy with eyelid myoclonia: Results of an international expert consensus panel.

OBJECTIVE: The objective of this study was to determine areas of consensus among an international panel of experts for the clinical presentation and diagnosis of epilepsy with eyelid myoclonia (EEM; formerly known as Jeavons syndrome) to improve a timely diagnosis. METHODS: An international steering committee was convened of physicians and patients/caregivers with expertise in EEM. This committee summarized the current literature and identified an international panel of experts (comprising 25 physicians and five patients/caregivers). This international expert panel participated in a modified Delphi process, including three rounds of surveys to determine areas of consensus for the diagnosis of EEM. RESULTS: There was a strong consensus that EEM is a female predominant generalized epilepsy syndrome with onset between 3 and 12 years of age and that eyelid myoclonia must be present to make the diagnosis. There was a strong consensus that eyelid myoclonia may go unrecognized for years prior to an epilepsy diagnosis. There was consensus that generalized tonic-clonic and absence seizures are typically or occasionally seen in patients. There was a consensus that atonic or focal seizures should lead to the consideration of reclassification or alternate diagnoses. There was a strong consensus that electroencephalography is required, whereas magnetic resonance imaging is not required for diagnosis. There was a strong consensus to perform genetic testing (either epilepsy gene panel or whole exome sequencing) when one or a combination of factors was present: family history of epilepsy, intellectual disability, or drug-resistant epilepsy. SIGNIFICANCE: This international expert panel identified multiple areas of consensus regarding the presentation and evaluation of EEM. These areas of consensus may be used to inform clinical practice to shorten the time to the appropriate diagnosis.

Differential diagnosis of familial adult myoclonic epilepsy.

OBJECTIVE: Familial adult myoclonic epilepsy (FAME) is an under-recognized disorder characterized by cortical myoclonus, generalized tonic-clonic seizures, and additional clinical symptoms, which vary depending on the FAME subtype. FAME is caused by pentanucleotide repeat expansions of intronic TTTCA/TTTTA in different genes. FAME should be distinguished from a range of differential diagnoses. METHODS: The differential diagnoses and frequent presentations leading to misdiagnosis of FAME were investigated from the available literature and reported based on an expert opinion survey. RESULTS: The phenotypic features of FAME, including generalized tonic-clonic and myoclonic seizures, are also seen in other epilepsy syndromes, such as juvenile myoclonic epilepsy, with a resultant risk of misdiagnosis and lack of identification of the underlying cause. Cortical myoclonus may mimic essential tremor or drug-induced tremor. In younger individuals, the differential diagnosis includes progressive myoclonus epilepsies (PMEs), such as Unverricht-Lundborg disease, whereas, in adulthood, late-onset variants of PMEs, such as sialidoses, myoclonus epilepsy, and ataxia due to potassium channel pathogenic variants should be considered. PMEs may also be suggested by cognitive impairment, cerebellar signs, or psychiatric disorders. Electroencephalography (EEG) may show similarities to other idiopathic generalized epilepsies or PMEs, with generalized spike-wave activity. Signs of cortical hyperexcitability may be seen, such as an increased amplitude of somatosensory evoked potentials or enhanced cortical reflex to sensory stimuli, together with the neurophysiological pattern of the movement disorder. SIGNIFICANCE: Recognition of FAME will inform prognostic and genetic counseling and diagnosis of the insidious progression, which may occur in older individuals who show mild cognitive deterioration. Distinguishing FAME from other disorders in individuals or families with this constellation of symptoms is essential to allow the identification of underlying etiology.

Biallelic variants in SLC38A3 encoding a glutamine transporter cause epileptic encephalopathy.

The solute carrier (SLC) superfamily encompasses >400 transmembrane transporters involved in the exchange of amino acids, nutrients, ions, metals, neurotransmitters and metabolites across biological membranes. SLCs are highly expressed in the mammalian brain; defects in nearly 100 unique SLC-encoding genes (OMIM: https://www.omim.org) are associated with rare Mendelian disorders including developmental and epileptic encephalopathy and severe neurodevelopmental disorders. Exome sequencing and family-based rare variant analyses on a cohort with neurodevelopmental disorders identified two siblings with developmental and epileptic encephalopathy and a shared deleterious homozygous splicing variant in SLC38A3. The gene encodes SNAT3, a sodium-coupled neutral amino acid transporter and a principal transporter of the amino acids asparagine, histidine, and glutamine, the latter being the precursor for the neurotransmitters GABA and glutamate. Additional subjects with a similar developmental and epileptic encephalopathy phenotype and biallelic predicted-damaging SLC38A3 variants were ascertained through GeneMatcher and collaborations with research and clinical molecular diagnostic laboratories. Untargeted metabolomic analysis was performed to identify novel metabolic biomarkers. Ten individuals from seven unrelated families from six different countries with deleterious biallelic variants in SLC38A3 were identified. Global developmental delay, intellectual disability, hypotonia, and absent speech were common features while microcephaly, epilepsy, and visual impairment were present in the majority. Epilepsy was drug-resistant in half. Metabolomic analysis revealed perturbations of glutamate, histidine, and nitrogen metabolism in plasma, urine, and CSF of selected subjects, potentially representing biomarkers of disease. Our data support the contention that SLC38A3 is a novel disease gene for developmental and epileptic encephalopathy and illuminate the likely pathophysiology of the disease as perturbations in glutamine homeostasis.

Association of symptoms of psychiatric disease and electroencephalographic patterns in idiopathic generalized epilepsy.

OBJECTIVE: Idiopathic generalized epilepsies (IGE) are genetic epilepsies with alterations of thalamo-frontocortical circuits that play a major role in seizure generation and propagation. Psychiatric diseases and drug resistance are strongly associated, but it remains unknown if they are symptoms of the same pathophysiological process. Hypothesizing that the same network alterations are associated with the frequency of epileptic discharges (ED) and psychiatric symptoms, we here tested the association of self-reported psychiatric symptoms and IGE severity estimated by electroencephalographic (EEG) biomarkers. METHODS: Idiopathic generalized epilepsies patients were asked to fill out four validated psychiatric screening tools assessing symptoms of personality disorders (Standard Assessment of Personality- Abbreviated Scale), depression (Major Depression Inventory), impulsiveness (Barratt Impulsiveness Scale), and anxiety (brief Epilepsy Anxiety Survey Instrument). Blinded to results and clinical data on the patients, we analyzed the patients' EEGs, assessed, and quantified ED. The number and duration of ED divided by the duration of the EEG served as a proxy for the severity of IGE that was correlated with the results of the psychiatric screening. RESULTS: Paired data from 64 patients were available for analysis. The duration of EDs per minute EEG was inversely associated with the time since the last seizure. The number of patients with generalized polyspike trains (n = 2), generalized paroxysmal fast activity (n = 3), and prolonged epileptiform discharges (n = 10) were too low for statistically meaningful analyses. Self-reported symptoms of depression, personality disorder, and impulsivity were not associated with EDs. In contrast, the duration of EDs per minute EEG was associated with self-reported symptoms of anxiety in univariate analyses, not significant, however, following adjustment for time since the last seizure in regression models. SIGNIFICANCE: Self-reported symptoms of psychiatric diseases were not strongly associated with EDs as the best available quantifiable biomarker of IGE severity. As expected, the duration of EDs per minute and anxiety was inversely associated with time since the last seizure. Our data argue against a direct link between the frequency of EDs - as an objective proxy of IGE severity - and psychiatric symptoms.

Reading epilepsy today: A scoping review and meta-analysis of reports of the last three decades.

BACKGROUND AND AIM: Reading-induced seizures are presumed to be rare phenomena attributed to an epilepsy syndrome not clearly belonging to either focal or generalized epilepsies. The aim of the article was to summarize knowledge and recent developments in the field of reading-induced seizures by reviewing all cases for which data were reported within the last three decades. METHODS: A scoping systematic review of demographic, clinical, electroencephalography (EEG) and imaging data of cases with reading-induced seizures reported in PubMed and Web of Science between 1991-01-01 and 2022-08-21 and a meta-analysis of the findings. RESULTS: The review included 101 case reports of epilepsy with reading-induced seizures (EwRIS) from 42 articles. The phenomenon was more prevalent among males (67, 66.3% vs. 34, 33.7%) with an average age of onset of 18.3 ± 7.9 years. When reported, 30.8% of patients had a family history of epilepsy. Orofacial reflex myocloni (ORM) were the most frequent manifestation (68, 67.3% cases), other presentations, mostly in addition to ORM, included visual, sensory or cognitive symptoms, non-orofacial myoclonic seizures, and absence seizures. Within the sample, 75 (74.3%) patients were identified as having primary reading epilepsy (PRE), 13 (12.9%) idiopathic generalised epilepsy (IGE) and 13 (12.9%) focal epilepsies. Advanced EEG and functional imaging data suggest that the basic mechanism of reading-induced seizures is probably similar despite different symptoms and consists of upregulation of the complex cerebral subsystem involved in reading. Ictogenesis and resulting symptomatology may then depend on predominant sensory or proprioceptive stimuli during reading. CONCLUSION: In most cases, reading-induced seizures were confirmed to belong to a particular epilepsy syndrome of PRE. However, there were substantial subgroups with IGE and focal epilepsies. Most likely, reading-induced seizures occur as an abnormal response to extero- or proprioceptive input into an upregulated cortical network subserving reading. Most recent researchers consider EwRIS a system epilepsy.

Genomic analysis of "microphenotypes" in epilepsy.

Large international consortia examining the genomic architecture of the epilepsies focus on large diagnostic subgroupings such as "all focal epilepsy" and "all genetic generalized epilepsy". In addition, phenotypic data are generally entered into these large discovery databases in a unidirectional manner at one point in time only. However, there are many smaller phenotypic subgroupings in epilepsy, many of which may have unique genomic risk factors. Such a subgrouping or "microphenotype" may be defined as an uncommon or rare phenotype that is well recognized by epileptologists and the epilepsy community, and which may or may not be formally recognized within the International League Against Epilepsy classification system. Here we examine the genetic structure of a number of such microphenotypes and report in particular on two interesting clinical phenotypes, Jeavons syndrome and pediatric status epilepticus. Although no single gene reached exome-wide statistical significance to be associated with any of the diagnostic categories, we observe enrichment of rare damaging variants in established epilepsy genes among Landau-Kleffner patients (GRIN2A) and pediatric status epilepticus patients (MECP2, SCN1A, SCN2A, SCN8A).

Brainwaves beyond diagnosis: Wider applications of electroencephalography in idiopathic generalized epilepsy.

Electroencephalography (EEG) has long been used as a versatile and noninvasive diagnostic tool in epilepsy. With the advent of digital EEG, more advanced applications of EEG have emerged. Compared with technologically advanced practice in focal epilepsies, the utilization of EEG in idiopathic generalized epilepsy (IGE) has been lagging, often restricted to a simple diagnostic tool. In this narrative review, we provide an overview of broader applications of EEG beyond this narrow scope, discussing how the current clinical and research applications of EEG may potentially be extended to IGE. The current literature, although limited, suggests that EEG can be used in syndromic classification, guiding antiseizure medication therapy, predicting prognosis, unraveling biorhythms, and investigating functional brain connectivity of IGE. We emphasize the need for longer recordings, particularly 24-h ambulatory EEG, to capture discharges reflecting circadian and sleep-wake cycle-associated variations for wider EEG applications in IGE. Finally, we highlight the challenges and limitations of the current body of literature and suggest future directions to encourage and enhance more extensive applications of this potent tool.

Headache in idiopathic/genetic epilepsy: Cluster analysis in a large cohort.

OBJECTIVE: The link between headache and epilepsy is more prominent in patients with idiopathic/genetic epilepsy (I/GE). We aimed to investigate the prevalence of headache and to cluster patients with regard to their headache and epilepsy features. METHODS: Patients aged 6-40 years, with a definite diagnosis of I/GE, were consecutively enrolled. The patients were interviewed using standardized epilepsy and headache questionnaires, and their headache characteristics were investigated by experts in headache. Demographic and clinical variables were analyzed, and patients were clustered according to their epilepsy and headache characteristics using an unsupervised K-means algorithm. RESULTS: Among 809 patients, 508 (62.8%) reported having any type of headache; 87.4% had interictal headache, and 41.2% had migraine. Cluster analysis revealed two distinct groups for both adults and children/adolescents. In adults, subjects having a family history of headache, ≥5 headache attacks, duration of headache ≥ 24 months, headaches lasting ≥1 h, and visual analog scale scores > 5 were grouped in one cluster, and subjects with juvenile myoclonic epilepsy (JME), myoclonic seizures, and generalized tonic-clonic seizures (GTCS) were clustered in this group (Cluster 1). Self-limited epilepsy with centrotemporal spikes and epilepsy with GTCS alone were clustered in Cluster 2 with the opposite characteristics. For children/adolescents, the same features as in adult Cluster 1 were clustered in a separate group, except for the presence of JME syndrome and GTCS alone as a seizure type. Focal seizures were clustered in another group with the opposite characteristics. In the entire group, the model revealed an additional cluster, including patients with the syndrome of GTCS alone (50.51%), with ≥5 attacks, headache lasting >4 h, and throbbing headache; 65.66% of patients had a family history of headache in this third cluster (n = 99). SIGNIFICANCE: Patients with I/GE can be clustered into distinct groups according to headache features along with seizures. Our findings may help in management and planning for future studies.

ILAE definition of the Idiopathic Generalized Epilepsy Syndromes: Position statement by the ILAE Task Force on Nosology and Definitions.

In 2017, the International League Against Epilepsy (ILAE) Classification of Epilepsies described the "genetic generalized epilepsies" (GGEs), which contained the "idiopathic generalized epilepsies" (IGEs). The goal of this paper is to delineate the four syndromes comprising the IGEs, namely childhood absence epilepsy, juvenile absence epilepsy, juvenile myoclonic epilepsy, and epilepsy with generalized tonic-clonic seizures alone. We provide updated diagnostic criteria for these IGE syndromes determined by the expert consensus opinion of the ILAE's Task Force on Nosology and Definitions (2017-2021) and international external experts outside our Task Force. We incorporate current knowledge from recent advances in genetic, imaging, and electroencephalographic studies, together with current terminology and classification of seizures and epilepsies. Patients that do not fulfill criteria for one of these syndromes, but that have one, or a combination, of the following generalized seizure types: absence, myoclonic, tonic-clonic and myoclonic-tonic-clonic seizures, with 2.5-5.5 Hz generalized spike-wave should be classified as having GGE. Recognizing these four IGE syndromes as a special grouping among the GGEs is helpful, as they carry prognostic and therapeutic implications.

Drug-resistant epilepsy at the age extremes: Disentangling the underlying etiology.

The incidence of epilepsy is highest at the extreme age ranges: childhood and elderly age. The most common syndromes in these demographics - self-limited epilepsies of childhood and idiopathic generalized epilepsies in pediatric age, focal epilepsy with structural etiology in older people - are expected to be drug responsive. In this work, we focus on such epilepsy types, overviewing the complex clinical background of unexpected drug-resistance. For self-limited epilepsies of childhood and idiopathic generalized epilepsies, we illustrate drug-resistance resulting from syndrome misinterpretation, reason on possible unexpected courses of epilepsy, and explicate the influence of inappropriate treatments. For elderly-onset epilepsy, we show the challenges in differential diagnosis possibly leading to pseudoresistance and analyze how drug-resistant epilepsy can arise in stroke, neurocognitive disorders, brain tumors, and autoimmune encephalitis. In children and senior people, drug-resistance can be regarded as a hint to review the diagnosis or explore alternative therapeutic strategies. Refractory seizures are not only a therapeutic challenge, but also a cardinal sign not to be overlooked in syndromes commonly deemed to be drug-responsive.

Electroclinical spectrum of generalized paroxysmal fast activity in adults without epileptic encephalopathy.

INTRODUCTION: Generalized paroxysmal fast activity (GPFA) is a rare and underreported EEG pattern known to be related to epileptic encephalopathy. We aimed to investigate the electroclinical spectrum of GPFA along with other atypical EEG features in patients without epileptic encephalopathy in routine EEG practice. METHODS: Outpatient EEG records of Hacettepe University Hospital were retrospectively reviewed between 2010 and 2020. Patients ≥ 18 years old with GPFA without epileptic encephalopathy were included. Electroclinical features of GPFA were analyzed. Atypical EEG features, epileptiform K-complexes and sleep spindles, and generalized polyspike train (GPT) were also investigated in this cohort. RESULTS: Most of the 19 included patients had refractory epilepsy (68%), while 16% were seizure-free. Generalized epilepsy (GE) was present in 58% of patients, and the rest had structural-focal epilepsy (26%), combined generalized and focal epilepsy (11%), or childhood occipital epilepsy (COE) (5%). Atypical EEG features with full atypical morphology were found in 91% of patients with GE. All patients with GPFA provoked by sleep had epileptiform K-complexes. The presence of GPT was not different between the GE and non-GE groups and was higher in patients with GPFA occurring only during sleep (p = 0.017). In two patients, GPFA frequency increased postictally. A transition from fixation-off sensitivity to GPFA occurred in a patient with COE. CONCLUSION: In this study, GPFA had a wide diagnostic range from focal to generalized epilepsy. The association of GPFA with other electroclinical features was of importance mostly for sleep outcomes; this finding might lead to a better understanding of epileptogenesis.

A Classification Model of EEG Signals Based on RNN-LSTM for Diagnosing Focal and Generalized Epilepsy.

Epilepsy is a chronic neurological disorder caused by abnormal neuronal activity that is diagnosed visually by analyzing electroencephalography (EEG) signals. BACKGROUND: Surgical operations are the only option for epilepsy treatment when patients are refractory to treatment, which highlights the role of classifying focal and generalized epilepsy syndrome. Therefore, developing a model to be used for diagnosing focal and generalized epilepsy automatically is important. METHODS: A classification model based on longitudinal bipolar montage (LB), discrete wavelet transform (DWT), feature extraction techniques, and statistical analysis in feature selection for RNN combined with long short-term memory (LSTM) is proposed in this work for identifying epilepsy. Initially, normal and epileptic LB channels were decomposed into three levels, and 15 various features were extracted. The selected features were extracted from each segment of the signals and fed into LSTM for the classification approach. RESULTS: The proposed algorithm achieved a 96.1% accuracy, a 96.8% sensitivity, and a 97.4% specificity in distinguishing normal subjects from subjects with epilepsy. This optimal model was used to analyze the channels of subjects with focal and generalized epilepsy for diagnosing purposes, relying on statistical parameters. CONCLUSIONS: The proposed approach is promising, as it can be used to detect epilepsy with satisfactory classification performance and diagnose focal and generalized epilepsy.