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Epilepsy is one of the most commonly-seen neurological disorders, and both endoplasmic reticulum stress (ERS) and oxidative stress (OS) have been demonstrated to be associated with epileptic seizures. As one of the three endogenous thiol-containing amino acids, cysteine (Cys) is recognized not only as an important biomarker of various biological processes but also widely used as a significant additive in the food industry. However, the exact role that Cys plays in ERS has not been well answered up to now. In this paper, we reported the first flavone-based fluorescent probe (namely BFC) with nice endoplasmic reticulum (ER)-targeting ability, which was capable of monitoring Cys in a fast response (3.0 min), large stokes shift (130 nm) and low detection limit (10.4 nM). The recognition mechanism of Cys could be attributed to the addition-cyclization reaction involving a Cys residue and an acrylate group, resulting in the release of the strong excited-state intramolecular proton transfer (ESIPT) emission molecule of benzoflavonol (BF). The low cytotoxicity and good biocompatibility of the probe BFC allowed for monitoring the fluctuation of endogenous Cys levels under both ERS and OS processes, as well as in zebrafish models of epilepsy. Quantitative determination of Cys with the probe BFC was also achieved in three different food samples. Additionally, a probe-immersed test strips integrated with a smartphone device was successfully constructed for on-site colorimetric detection of Cys. Undoubtedly, our work provided a valuable tool for tracking Cys levels in both an epilepsy model and real food samples.

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Epilepsy claims the lives of many people, so researchers strive to build highly accurate diagnostic models. One of the limitations of obtaining high accuracy is the scarcity of Electroencephalography (EEG) data and the fact that they are from different devices in terms of the channels number and sampling frequency. The paper proposes universal epilepsy diagnoses with high accuracy from electroencephalography signals taken from any device. The novelty of the proposal is to convert VEEG video into images, separating some parts and unifying images taken from different devices. The images were tested by dividing the video into labeled frames of different periods. By adding the spatial attention layer to the deep learning in the new model, classification accuracy increased to 99.95 %, taking five seconds/frame. The proposed has high accuracy in detecting epilepsy from any EEG without being restricted to a specific number of channels or sampling frequencies.

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BACKGROUND: Activation procedures (APs) are adopted during routine electroencephalography (rEEG) to provoke interictal epileptiform abnormalities (EAs). This study aimed to observe interictal and ictal (EAs) of different EEG patterns, provoked by various APs. METHODOLOGY: This cross-sectional study was performed in the neurology department of King Fahd hospital of university, Saudi Arabia. The EEGs and medical records of patients who presented for EEG recordings were screened initially, then 146 EEGs provoked EAs due to utilization of APs, were included for analysis. RESULTS: Among all EEGs with provoked EAs, Non-rapid eye movement sleep (NREM) provoked EAs in 93 (63.7%) patients with following patterns, focal spike wave discharges (FSWDs) 45 (P= 0.01), focal spike wave discharges with bilateral synchrony (FSWDBS) 27 (P=0.03) and generalized spike wave discharges (GSWDs) 46 (P=0.01). Intermittent photic stimulation (IPS) most significantly provoked FSWDs in 07 patient (P =0.01) and GSWDs in 30 patients (P=<0.001) 7 patients (P = 0.01) and GSWDs in 30 patients (P < 0.001). Hyperventilation (HV) was associated with a higher occurrence of GSWDs in 37 patients (P =0.01). Female sex 7 (P = 0.02), provoked GSWDs 3 (P = 0.03), NREM sleep 8 (P = 0.04), prolonged EEG record 3 (P = 0.02), clinical events during recording 5 (P ≤ 0.01), diagnosis of genetic 05 (P = 0.03), and immune-mediated epilepsies 2 (P = 0.001) were associated with the provocation of ictal EAs; however, in multiple logistic regression analysis, no statistically significant association of these variables (P ≥ 0.05 each) was noted. CONCLUSION: The provocation of EAs in rEEG with different APs varies according to circumstances, including seizure types, epilepsy etiology, and the type of AP applied. These clinical and procedural parameters affect the diagnostic yield of rEEG and need careful consideration during rEEG recordings. APs adopted during rEEG recording can induce FSWDs, FSWDBS, and GSWDs in the form of either interictal or ictal EAs in various etiologies of epilepsy. Ictal EAs may appear in the form of GSWDs, during NREM sleep, in prolonged EEG records; however, their independent association needs to be evaluated in larger sample studies. Further, prospective cohort studies with adequate sample sizes are warranted.

Résumé Contexte:Des procédures d'activation (AP) sont adoptées lors d'une électroencéphalographie de routine (rEEG) pour provoquer des anomalies épileptiformes (EA) intercritiques. Cette étude visait à observer les inter-critiques et critiques (EA) de différents modèles EEG, provoqués par divers PA.Méthodes:Cette étude transversale a été réalisée dans le département de neurologie de l'hôpital universitaire King Fahd de Khobar, en Arabie Saoudite. Les EEG et les dossiers médicaux des patients qui se sont présentés pour des enregistrements EEG ont été initialement examinés, puis 146 EEG avec des EA provoqués lors de l'utilisation des AP ont été inclus pour analyse.Résultats:Parmi tous les EEG avec des AE provoqués, le sommeil à mouvements oculaires non rapides (NREM) a provoqué des EA chez 93 (63,7 %) patients avec les schémas suivants : décharges d'ondes de pointe focales (FSWD) 45 (P = 0,01), onde de pointe focale avec bilatéral synchronisation (FSWBS) 27 (P = 0,03) et décharges d'ondes de pointe généralisées (GSWD) 46 (P = 0,01). La stimulation photique intermittente (IPS) a provoqué de manière plus significative des FSWD chez 07 patients (P = 0,01) et des GSWD chez 30 patients (P = < 0,001) 7 patients (P = 0,01) et des GSWD chez 30 patients (P < 0,001). L'hyperventilation (HV) était associée à une fréquence plus élevée de GSWD chez 37 patients (P = 0,01). Sexe féminin 07 (P = 0,02), GSWD provoqués 03 (P = 0,03), sommeil NREM 08 (P = 0,04), enregistrement EEG prolongé 03 (P = 0,02), événements cliniques lors de l'enregistrement 05 (P = < 0,01), diagnostic des épilepsies génétiques 05 (P = 0,03) et des épilepsies à médiation immunitaire 02 (P = 0,001) étaient associées à la provocation d'EA critiques, cependant, dans l'analyse de régression logistique multiple, aucune association statistiquement significative de ces variables (P = > 0,05 chacune) était noté.Conclusion:La provocation d'EA dans l'EEGr avec différents AP varie en fonction des circonstances, notamment des types de crises, de l'étiologie de l'épilepsie et du type d'AP appliqué. Ces paramètres cliniques et procéduraux affectent le rendement diagnostique du rEEG et doivent être soigneusement pris en compte lors des enregistrements rEEG. Les AP adoptés lors de l'enregistrement rEEG peuvent induire des FSWD, des FSWBS et des GSWD sous la forme d'EA inter-critiques ou critiques dans diverses étiologies de l'épilepsie. Les EA critiques peuvent apparaître sous forme de GSWD, pendant le sommeil NREM, dans les enregistrements EEG prolongés; cependant, leur association indépendante doit être évaluée dans des études sur un échantillon plus large. De plus, des études de cohortes prospectives avec des échantillons de taille adéquate sont justifiées.

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This paper comprehensively reviews hardware acceleration techniques and the deployment of convolutional neural networks (CNNs) for analyzing electroencephalogram (EEG) signals across various application areas, including emotion classification, motor imagery, epilepsy detection, and sleep monitoring. Previous reviews on EEG have mainly focused on software solutions. However, these reviews often overlook key challenges associated with hardware implementation, such as scenarios that require a small size, low power, high security, and high accuracy. This paper discusses the challenges and opportunities of hardware acceleration for wearable EEG devices by focusing on these aspects. Specifically, this review classifies EEG signal features into five groups and discusses hardware implementation solutions for each category in detail, providing insights into the most suitable hardware acceleration strategies for various application scenarios. In addition, it explores the complexity of efficient CNN architectures for EEG signals, including techniques such as pruning, quantization, tensor decomposition, knowledge distillation, and neural architecture search. To the best of our knowledge, this is the first systematic review that combines CNN hardware solutions with EEG signal processing. By providing a comprehensive analysis of current challenges and a roadmap for future research, this paper provides a new perspective on the ongoing development of hardware-accelerated EEG systems.

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Advances in genetics led to the identification of hundreds of epilepsy-related genes, some of which are treatable with etiology-specific interventions. However, the diagnostic yield of next-generation sequencing (NGS) in unexplained epilepsy is highly variable (10-50%). We sought to determine the diagnostic yield and clinical utility of NGS in children with unexplained epilepsy that is accompanied by neurodevelopmental delays and/or is medically intractable. A 5-year retrospective review was conducted at the American University of Beirut Medical Center to identify children who underwent whole exome sequencing (WES) or whole genome sequencing (WGS). Data on patient demographics, neurodevelopment, seizures, and treatments were collected. Forty-nine children underwent NGS with an overall diagnostic rate of 68.9% (27/38 for WES, and 4/7 for WGS). Most children (42) had neurodevelopmental delays with (18) or without (24) refractory epilepsy, and only three had refractory epilepsy without delays. The diagnostic yield was 77.8% in consanguineous families (18), and 61.5% in non-consanguineous families (26); consanguinity information was not available for one family. Genetic test results led to anti-seizure medication optimization or dietary therapies in six children, with subsequent improvements in seizure control and neurodevelopmental trajectories. Not only is the diagnostic rate of NGS high in children with unexplained epilepsy and neurodevelopmental delays, but also genetic testing in this population may often lead to potentially life-altering interventions.

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AIM: To determine changes in the quality of life associated with epilepsy in school-age children with newly diagnosed uncomplicated epilepsy over the first six months after diagnosis to find points relevant for the early prevention of deterioration in quality of life. METHODS: This prospective follow-up study, performed in University Children's Hospital in Belgrade, enrolled 60 school-aged children with recently diagnosed epilepsy, along with their parents. The respondents completed the Children with Epilepsy Quality of Life immediately following the diagnosis of epilepsy and six months later. RESULTS: Significant decline was observed in the domains related to intrapersonal/emotional relationships by both children (P<0.001) and their parents (P=0.03), and in the need to keep epilepsy a secret as observed by parents (P = 0.04). Significant improvement was found in the Interpersonal/Social domain as rated by parents (P=0.001). Total quality-of-life scores, as assessed by children and parents, did not change significantly. CONCLUSION: Bearing in mind that stigma and intrapersonal struggles are the major factors affecting the quality of life in children with epilepsy, psychological and social support is highly recommended in the first six months following an epilepsy diagnosis. Since intrapersonal relationships improved over six months, compensating for other deteriorations in the quality of life, children with epilepsy should be encouraged to socialize with their peers and to join organizations and actions that encourage social contact.

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Developmental and epileptic encephalopathies, the most severe group of epilepsies, are characterized by seizures and frequent epileptiform activity associated with developmental slowing or regression. Onset typically occurs in infancy or childhood and includes many well-defined epilepsy syndromes. Patients have wide-ranging comorbidities including intellectual disability, psychiatric features, such as autism spectrum disorder and behavioural problems, movement and musculoskeletal disorders, gastrointestinal and sleep problems, together with an increased mortality rate. Problems change with age and patients require substantial support throughout life, placing a high psychosocial burden on parents, carers and the community. In many patients, the aetiology can be identified, and a genetic cause is found in >50% of patients using next-generation sequencing technologies. More than 900 genes have been identified as monogenic causes of developmental and epileptic encephalopathies and many cell components and processes have been implicated in their pathophysiology, including ion channels and transporters, synaptic proteins, cell signalling and metabolism and epigenetic regulation. Polygenic risk score analyses have shown that common variants also contribute to phenotypic variability. Holistic management, which encompasses antiseizure therapies and care for multimorbidities, is determined both by epilepsy syndrome and aetiology. Identification of the underlying aetiology enables the development of precision medicines to improve the long-term outcome of patients with these devastating diseases.

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OBJECTIVE: To describe how smartphone applications can contribute to the management of epilepsy in children and adolescents. DATA SOURCE: This is an integrative review conducted on the Medline, PubMed, and SciELO databases, based on the descriptors "epilepsy" and "smartphone." Original studies published between 2017-2023 in Portuguese or English that addressed the research question were included. Theses and dissertations, duplicate studies, literature reviews, and studies that did not answer the research question were excluded. DATA SYNTHESIS: A total of 178 studies were located, of which six were selected for this review. The sample included 731 participants (631 children and adolescents with epilepsy and 100 caregivers). The applications allow for the collection of seizure frequency; timing and type of crisis; reminders for medication administration; and information about sleep quality. They can store these data for healthcare professionals, caregivers, and users to monitor the progress of the condition. CONCLUSIONS: The use of applications in managing seizures in children and adolescents with epilepsy shows promising results by promoting continuous and personalized monitoring. Further studies are needed to optimize beneficial outcomes and overcome challenges.

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This study aims to develop a detection method based on morphological features of spike-wave (SW) patterns in the EEG of epilepsy patients and evaluate the effect of cathodal transcranial direct current stimulation (ctDCS) treatment. The proposed method is based on several simple features describing the shape of SW patterns and their synchronous occurrence on at least two EEG channels. High sensitivity, specificity and selectivity values were achieved for each patient and condition. ctDCS resulted in a significant reduction in the number of detected patterns, a decrease in spike duration and amplitude, and an increased spike mobility. The proposed method allows efficient identification of SW patterns regardless of brain condition, although the recruitment of patterns may be modified by ctDCS. This method can be useful in the clinical evaluation of ctDCS effects.

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BACKGROUND: Epilepsy and other seizure disorders account for a high disease burden in Germany. As a timely diagnosis and accurate treatment are crucial, improving the management of these disorders is important. Outside of Germany, outpatient long-term video EEGs (ALVEEGs) have demonstrated the potential to support the diagnosis and management of epilepsy and other seizure disorders. This study aims to evaluate the implementation of ALVEEGs as a new diagnostic pathway in eastern parts of Germany to diagnose epilepsy and other seizure disorders and to assess if ALVEEGs are equally effective as the current inpatient-monitoring gold standard, which is currently only available at a limited number of specialized centers in Germany. METHODS: ALVEEG is a prospective, multicenter, randomized controlled equivalence trial, involving five epilepsy centers in the eastern states of Germany. Patients will be randomized into either intervention (IG) or control group (CG), using a permuted block randomization. The sample size targeted is 688 patients, continuously recruited over the trial. The IG will complete an ALVEEG in a home setting, including getting access to a smartphone app to document seizure activity. The CG will receive care as usual, i.e., inpatient long-term video-EEG monitoring. The primary outcome is the proportion of clinical questions being solved in the IG compared to the CG. Secondary outcomes include hospital stays, time until video EEG, time until diagnosis and result discussion, patients' health status, quality of life and health competence, and number and form of epilepsy-related events and epileptiform activity. Alongside the trial, a process implementation and health economic evaluation will be conducted. DISCUSSION: The extensive evaluation of this study, including an implementation and health economic evaluation, will provide valuable information for health policy decision-makers to optimize future delivery of neurological care to patients affected by epilepsy and other seizure disorders and on the uptake of ALVEEG into standard care in Germany. TRIAL REGISTRATION: German Clinical Trials Register (DRKS00032220), date registered: December 11, 2023.

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Epilepsy is a chronic disease caused by repeated abnormal discharge of neurons in the brain. Accurately predicting the onset of epilepsy can effectively improve the quality of life for patients with the condition. While there are many methods for detecting epilepsy, EEG is currently considered one of the most effective analytical tools due to the abundant information it provides about brain activity. The aim of this study is to explore potential time-frequency and channel features from multi-channel epileptic EEG signals and to develop a patient-specific seizure prediction network. In this paper, an epilepsy EEG signal classification algorithm called Channel Recurrent Criss-cross Attention Network (CRCANet) is proposed. Firstly, the spectrograms processed by the short-time fourier transform is input into a Convolutional Neural Network (CNN). Then, the spectrogram feature map obtained in the previous step is input into the channel attention module to establish correlations between channels. Subsequently, the feature diagram containing channel attention characteristics is input into the recurrent criss-cross attention module to enhance the information content of each pixel. Finally, two fully connected layers are used for classification. We validated the method on 13 patients in the public CHB-MIT scalp EEG dataset, achieving an average accuracy of 93.8 %, sensitivity of 94.3 %, and specificity of 93.5 %. The experimental results indicate that CRCANet can effectively capture the time-frequency and channel characteristics of EEG signals while improving training efficiency.

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Epilepsy is one of the most common brain diseases, characterised by repeated seizures that occur on a regular basis. During a seizure, a patient's muscles flex uncontrollably, causing a loss of mobility and balance, which can be harmful or even fatal. Developing an automatic approach for warning patients of oncoming seizures necessitates substantial research. Analyzing the electroencephalogram (EEG) output from the human brain's scalp region can help predict seizures. EEG data were analyzed to extract time domain features such as Hurst exponent (Hur), Tsallis entropy (TsEn), enhanced permutation entropy (impe), and amplitude-aware permutation entropy (AAPE). In order to automatically diagnose epileptic seizure in children from normal children, this study conducted two sessions. In the first session, the extracted features from the EEG dataset were classified using three machine learning (ML)-based models, including support vector machine (SVM), K nearest neighbor (KNN), or decision tree (DT), and in the second session, the dataset was classified using three deep learning (DL)-based recurrent neural network (RNN) classifiers in The EEG dataset was obtained from the Neurology Clinic of the Ibn Rushd Training Hospital. In this regard, extensive explanations and research from the time domain and entropy characteristics demonstrate that employing GRU, LSTM, and BiLSTM RNN deep learning classifiers on the All-time-entropy fusion feature improves the final classification results.

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Objective.For medically-refractory epilepsy patients, stereoelectroencephalography (sEEG) is a surgical method using intracranial electrode recordings to identify brain networks participating in early seizure organization and propagation (i.e. the epileptogenic zone, EZ). If identified, surgical EZ treatment via resection, ablation or neuromodulation can lead to seizure-freedom. To date, quantification of sEEG data, including its visualization and interpretation, remains a clinical and computational challenge. Given elusiveness of physical laws or governing equations modelling complex brain dynamics, data science offers unique insight into identifying unknown patterns within high-dimensional sEEG data. We apply here an unsupervised data-driven algorithm, dynamic mode decomposition (DMD), to sEEG recordings from five focal epilepsy patients (three with temporal lobe, and two with cingulate epilepsy), who underwent subsequent resective or ablative surgery and became seizure free.Approach.DMD obtains a linear approximation of nonlinear data dynamics, generating coherent structures ('modes') defining important signal features, used to extract frequencies, growth rates and spatial structures. DMD was adapted to produce dynamic modal maps (DMMs) across frequency sub-bands, capturing onset and evolution of epileptiform dynamics in sEEG data. Additionally, we developed a static estimate of EZ-localized electrode contacts, termed the higher-frequency mode-based norm index (MNI). DMM and MNI maps for representative patient seizures were validated against clinical sEEG results and seizure-free outcomes following surgery.Main results.DMD was most informative at higher frequencies, i.e. gamma (including high-gamma) and beta range, successfully identifying EZ contacts. Combined interpretation of DMM/MNI plots best identified spatiotemporal evolution of mode-specific network changes, with strong concordance to sEEG results and outcomes across all five patients. The method identified network attenuation in other contacts not implicated in the EZ.Significance.This is the first application of DMD to sEEG data analysis, supporting integration of neuroengineering, mathematical and machine learning methods into traditional workflows for sEEG review and epilepsy surgical decision-making.

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BACKGROUND: Genetic epilepsy diagnosis is increasing due to technological advancements. Although the use of molecular diagnosis is increasing, chromosomal microarray analysis (CMA) remains an important diagnostic tool for many patients. We aim to explore the role and indications of CMA in epilepsy, given the current genomic advances. METHODS: We obtained data from 378 epileptic described patients, who underwent CMA between 2015 and 2021. Different types of syndromic or nonsyndromic epilepsy were represented. RESULTS: After excluding patients who were undertreated or had missing data, we included 250 patients with treated epilepsy and relevant clinical information. These patients mostly had focal epilepsy or developmental and epileptic encephalopathy, with a median start age of 2 years. Ninety percent of the patients had intellectual disability, more than two thirds had normal head size, and 60% had an abnormal magnetic resonance imaging. We also included 10 patients with epilepsy without comorbidities. In our cohort, we identified 35 pathogenic copy number variations (CNVs) explaining epilepsy with nine recurrent CNVs enriched in patients with epilepsy, 12 CNVs related to neurodevelopmental disorder phenotype with possible epilepsy, five CNVs including a gene already known in epilepsy, and nine CNVs based on size combined with de novo occurrence. The diagnosis rate in our study reached 14% (35 of 250) with first-line CMA, as previously reported. Although targeted gene panel sequencing could potentially diagnose some of the reported epilepsy CNVs (34% [12 of 35]). CONCLUSIONS: CMA remains a viable option as the first-line genetic test in cases where other genetic tests are not available and as a second-line diagnostic technique if gene panel or exome sequencing yields negative results.

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OBJECTIVE: While evoked potentials elicited by single pulse electrical stimulation (SPES) may assist seizure onset zone (SOZ) localization during intracranial EEG (iEEG) monitoring, induced high frequency activity has also shown promising utility. We aimed to predict SOZ sites using induced cortico-cortical spectral responses (CCSRs) as an index of excitability within epileptogenic networks. METHODS: SPES was conducted in 27 epilepsy patients undergoing iEEG monitoring and CCSRs were quantified by significant early (10-200 ms) increases in power from 10 to 250 Hz. Using response power as CCSR network connection strengths, graph centrality measures (metrics quantifying each site's influence within the network) were used to predict whether sites were within the SOZ. RESULTS: Across patients with successful surgical outcomes, greater CCSR centrality predicted SOZ sites and SOZ sites targeted for surgical treatment with median AUCs of 0.85 and 0.91, respectively. We found that the alignment between predicted and targeted SOZ sites predicted surgical outcome with an AUC of 0.79. CONCLUSIONS: These findings indicate that network analysis of CCSRs can be used to identify increased excitability of SOZ sites and discriminate important surgical targets within the SOZ. SIGNIFICANCE: CCSRs may supplement traditional passive iEEG monitoring in seizure localization, potentially reducing the need for recording numerous seizures.

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INTRODUCTION: Few studies demonstrated that focal epilepsy (FE) with left hemispheric (LH) seizure onset is more frequent than with right hemispheric (RH) seizure onset. In addition, patients with LH seizure onset show worse clinical course compared to those with RH seizure onset. The aim of our study was to investigate both issues in a great cohort of FE patients. METHODS: In the retrospective study, we reviewed the clinical and paraclinical data of 682 patients with exclusively LH or RH seizure onset. We ascertained the laterality of seizure onset mainly by ictal and postictal semiology and ictal EEG findings. In the absence of ictal data, the basis of lateralization was the evidence of unilateral structural brain abnormality together with a corresponding interictal EEG finding. The endpoint of analysis of the clinical course was the presence/absence of five-year remission on drug treatment in the first ten years of treatment. RESULTS: Out of the 682 patients, 378 (55.4 per cent) had LH and 304 (44.6 per cent) had RH seizure onset. The difference was statistically significant (p = 0.04). Out of them, 213 LH and 156 RH patients were eligible to evaluate prognosis. Five-years-remission was attained by 71 patients (33.3 per cent) in the LH, and 65 (41.7 per cent) in the RH group. The difference was statistically significant (p = 0.05). CONCLUSION: We demonstrated the LH dominance of seizure onset and the worse clinical course of the patients with LH seizure onset. The findings are manifestations of the lateralized epileptic propensity of the brain. The dissimilar clinical course of the patient with LH and RH seizure onset may shape the general prognostic scheme in FE patients.

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BACKGROUND: Novel mobile and portable EEG solutions, designed for short and long-term monitoring of individuals with epilepsy have been developed in recent years but, they are underutilized, lacking full integration into clinical routine. Exploring the opinions of hospital-based healthcare professionals regarding their potential application, technical requirements and value would be crucial for future device development and increase their clinical application. PURPOSE: To evaluate professionals' opinions on novel EEG systems, focusing on their potential application in various clinical settings, professionals' interest in non-invasive solutions for ultra-long monitoring of people with epilepsy (PWE) and factors which could increase future use of novel EEG systems. MATERIALS AND METHODS: We conducted an online survey where Hospital-based professionals shared opinions on potential advantages, clinical value, and key features of novel wearable EEG systems in five different clinical settings. Additionally, insights were gathered on the need for future research and, the need for additional information about devices from companies and researchers. RESULTS: Respondents (n = 40) prioritized high performance, data quality, easy patient mobility, and comfort as crucial features for novel devices. Advantages were highlighted, including more natural settings, reduced application time, earlier epilepsy diagnosis, and decreased support requirements. Novel EEG devices were seen as valuable for epilepsy diagnosis, seizure monitoring, automatic seizure documentation, seizure alarms, and seizure forecasting. Interest in integrating these new systems into clinical practice was high, particularly for supervising drug-resistant epilepsy, reducing SUDEP, and detecting nocturnal seizures. Professionals emphasized the need for more research studies and highlighted the need for increased information from companies and researchers. CONCLUSIONS: Professionals underscore specific technical and practical features, along with potential clinical advantages and value of novel EEG devices that could drive their development. While interest in integrating these solutions in clinical practice exists, further validation studies and enhanced communication between researchers, companies, and clinicians are crucial for overcoming potential scepticism and facilitating widespread adoption.

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Catatonia is currently conceived in the major diagnostic manuals as a syndrome with a range of possible psychiatric and general medical underlying conditions. It features diverse clinical signs, spanning motor, verbal and behavioural domains and including stupor, catalepsy, mutism, echolalia, negativism and withdrawal. The existing literature suggests that seizure activity may underlie catatonia in approximately 2% of cases. There are three possible temporal relationships between catatonia and seizure activity: (1) ictal catatonia, in which catatonia is a presentation of non-convulsive status epilepticus; (2) postictal catatonia, in which catatonia follows a seizure, and (3) interictal catatonia, in which catatonia and seizures occur in the same individual without any clear temporal relationship between them. Electroencephalographic (EEG) abnormalities are common in catatonia, even in those cases with a presumed primary psychiatric origin, and often consist of generalised background slowing. Paradoxically, electroconvulsive therapy is an effective treatment for catatonia. There are several converging pieces of evidence suggesting that there may be underlying seizure activity in more cases of catatonia than has hitherto been recognised, though identification of these seizures may require intracranial EEG recording.

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Sequence-based genetic testing identifies causative variants in ~ 50% of individuals with developmental and epileptic encephalopathies (DEEs). Aberrant changes in DNA methylation are implicated in various neurodevelopmental disorders but remain unstudied in DEEs. We interrogate the diagnostic utility of genome-wide DNA methylation array analysis on peripheral blood samples from 582 individuals with genetically unsolved DEEs. We identify rare differentially methylated regions (DMRs) and explanatory episignatures to uncover causative and candidate genetic etiologies in 12 individuals. Using long-read sequencing, we identify DNA variants underlying rare DMRs, including one balanced translocation, three CG-rich repeat expansions, and four copy number variants. We also identify pathogenic variants associated with episignatures. Finally, we refine the CHD2 episignature using an 850 K methylation array and bisulfite sequencing to investigate potential insights into CHD2 pathophysiology. Our study demonstrates the diagnostic yield of genome-wide DNA methylation analysis to identify causal and candidate variants as 2% (12/582) for unsolved DEE cases.

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