Hippocampal sclerosis, characterized by significant hippocampal neuronal loss, oxidative stress, glial cell proliferation, and inflammatory responses, constitutes a pivotal component in the pathogenesis of temporal lobe epilepsy (TLE). Traditional treatment strategies, mainly involving anti-epileptic drugs, face challenges including ineffectiveness, drug tolerance, and adverse reactions, complicating management of the condition. Herein, we design and engineer ultrasmall potassium calcium hexacyanoferrate (III) nanoparticles, designated as KCaHNPs, which feature a broad spectrum of enzymatic activities analogous to superoxide dismutase, catalase, peroxidase, and glutathione peroxidase. KCaHNPs efficiently neutralize excessive reactive oxygen species, mitigate mitochondrial dysfunction, maintain neuronal integrity, and prevent apoptosis. Importantly, KCaHNPs significantly reduce neuronal damage, apoptosis, ferroptosis, and glial cells activation in TLE-afflicted rats, thereby improving spatial and short-term memory, and diminishing epileptic hyperexcitability. Prophylactic deployment of KCaHNPs markedly decreases the frequency and duration of seizures, extends the latency period before the onset of initial seizures, and enhances neural functions within the hippocampal CA3 area. Collectively, these findings underscore the potent therapeutic and prophylactic efficacy of KCaHNPs in mitigating TLE by bolstering cellular defense mechanisms against oxidative stress and inflammation. This innovative approach holds promise as a comprehensive and efficacious strategy for managing temporal lobe epilepsy and potentially other complex neurological disorders.

To determine if implementing the IFCN criteria to define interictal epileptiform discharges (IEDs) improves expert inter-rater reliability (IRR) and diagnostic performance.

Nine EEG experts rated the same 200 candidate IEDs (100 expert-consensus, 100 epilepsy monitoring unit [EMU]-validated) as epileptiform or not, in random order, in two rounds separated by at least 30 days. During the second round, raters additionally selected the applicable IFCN criteria for each candidate IED.

Overall, there were no major differences in performance (AUC; 0.90 vs. 0.91) or IRR (AC1; 0.48 vs. 0.47) between both Parts; nor was there a major difference in calibration within the expert-consensus dataset (median absolute calibration index; 35.5 vs. 30.0). Similarly, there were no major differences in performance or IRR within either dataset. IRR was substantial within the EMU-validated dataset and only fair within the expert-consensus dataset. IRR was fair for criteria 2, 3, 5 and 6, and moderate for criteria 1 and 4.

Our findings suggest that the IFCN criteria to define IEDs may not significantly improve IRR, performance, or overall calibration among experts.

Increasing expert IRR for each criterion may enhance the utility of the IFCN criteria in clinical practice.

Whereas a scalp electroencephalogram (EEG) is important for diagnosing epilepsy, a single routine EEG is limited in its diagnostic value. Only a small percentage of routine EEGs show interictal epileptiform discharges (IEDs) and overall misdiagnosis rates of epilepsy are 20% to 30%. We aim to demonstrate how network properties in EEG recordings can be used to improve the speed and accuracy differentiating epilepsy from mimics, such as functional seizures - even in the absence of IEDs.

In this multicenter study, we analyzed routine scalp EEGs from 218 patients with suspected epilepsy and normal initial EEGs. The patients' diagnoses were later confirmed based on an epilepsy monitoring unit (EMU) admission. About 46% ultimately being diagnosed with epilepsy and 54% with non-epileptic conditions. A logistic regression model was trained using spectral and network-derived EEG features to differentiate between epilepsy and non-epilepsy. Of the 218 patients, 90% were used for training and 10% were held out for testing. Within the training set, 10-fold cross validation was performed. The resulting tool was named "EpiScalp."

EpiScalp achieved an area under the curve (AUC) of 0.940, an accuracy of 0.904, a sensitivity of 0.835, and a specificity of 0.963 in classifying patients as having epilepsy or not.

EpiScalp provides an accurate diagnostic aid from a single initial EEG recording, even in more challenging epilepsy cases with normal initial EEGs. This may represent a paradigm shift in epilepsy diagnosis by deriving an objective measure of epilepsy likelihood from previously uninformative EEGs. ANN NEUROL 2025;97:907-918.

Epilepsy, one of the most common neurological diseases in the world, affects around 50 million people, with a notably disproportionate prevalence in individuals residing in low- and middle-income countries (LMICs). Alarmingly, over 80% of annual epilepsy-related fatalities occur within LMICs. The burden of the disease assessed using Disability Adjusted Life Years (DALYs) shows that epilepsy accounts for about 13 million DALYs per year, with LMICs bearing most of this burden due to the disproportionately high diagnostic and treatment gaps. Furthermore, LMICs also endure a significant financial burden, with the cost of epilepsy reaching up to 0.5% of the Gross National Product (GNP) in some cases. Difficulties in the appropriate diagnosis and treatment are complicated by the lack of trained medical specialists. Therefore, in these conditions, adopting artificial intelligence (AI)-based solutions may improve epilepsy care in LMICs. In this theoretical and critical review, we focus on epilepsy and its management in LMICs, as well as on the employment of AI technologies to aid epilepsy care in LMICs. We begin with a general introduction of epilepsy and present basic diagnostic and treatment approaches. We then explore the socioeconomic impact, treatment gaps, and efforts made to mitigate these issues. Taking this step further, we examine recent AI-related developments and their potential as assistive tools in clinical application in LMICs, along with proposals for future directions. We conclude by suggesting the need for scalable, low-cost AI solutions that align with the local infrastructure, policy and community engagement to improve epilepsy care in LMICs.

Identification of seizures is essential for the treatment of epilepsy. Current machine-learning and deep-learning models often perform well on public datasets when classifying generalized seizures with prominent features. However, their performance was less effective in detecting brief, localized seizures. These seizure-like patterns can be masked by fixed brain rhythms.

Our study proposes a supervised multilayer hybrid model called GEM-CRAP (gradient-enhanced modulation with CNN-RES, attention-like, and pre-policy networks), with three parallel feature extraction channels: a CNN-RES module, an amplitude-aware channel with attention-like mechanisms, and an LSTM-based pre-policy layer integrated into the recurrent neural network. The model was trained on the Xuanwu Hospital and HUP iEEG dataset, including intracranial, cortical, and stereotactic EEG data from 83 patients, covering over 8500 labeled electrode channels for hybrid classification (wakefulness and sleep). A post-SVM network was used for secondary training on channels with classification accuracy below 80%. We introduced an average channel deviation rate metric to assess seizure detection accuracy.

For public datasets, the model achieved over 97% accuracy for intracranial and cortical EEG sequences in patients, and over 95% for mixed sequences, with deviations below 5%. In the Xuanwu Hospital dataset, it maintained over 94% accuracy for wakefulness seizures and around 90% during sleep. SVM secondary training improved average channel accuracy by over 10%. Additionally, a strong positive correlation was found between channel accuracy distribution and the temporal distribution of seizure states.

GEM-CRAP enhances focal epilepsy detection through adaptive adjustments and attention mechanisms, achieving higher precision and robustness in complex signal environments. Beyond improving seizure interval detection, it excels in identifying and analyzing specific epileptic waveforms, such as high-frequency oscillations. This advancement may pave the way for more precise epilepsy diagnostics and provide a suitable artificial intelligence algorithm for closed-loop neurostimulation.

To investigate the role, frequency, and pattern of signal changes in cranial MRI associated with status epilepticus (SE) and their correlation with EEG and clinical findings in patients with large lesions, such as tumors, strokes, or major space-occupying anomalies.

This retrospective cohort study included 44 patients diagnosed with SE between January 2013 and June 2019. Data on demographic and clinical characteristics, seizure semiology, SE features (type and prognosis), and EEG and MRI findings were collected from hospital records. The relationships between periictal MRI abnormalities, MRI lateralization, clinical semiology, EEG findings, SE prognosis, and outcome at discharge were analyzed.

The median age of participants was 61.5 years, with 65.91% being female. Bilateral MRI signals were significantly more common in patients with generalized convulsive SE. Patients with SWI signal changes had a significantly lower median age and a higher percentage of previous epilepsy history. Increased signal intensity on DWI and T2-FLAIR sequences was observed in 86.4% and 22.7% of patients, respectively. Among those with increased DWI signals, the neocortex was a common localization (45.45%). The group with T2-FLAIR signal increases had a significantly lower median age, a higher percentage of generalized convulsive SE, and a lower percentage of non-convulsive SE. Poor prognosis was observed in 40.91% of patients, with generalized EEG findings significantly more frequent in this group.

Periictal MRI findings in SE patients demonstrated significant associations with clinical presentation but showed no correlation with EEG or prognosis. Further research is needed to explore the link between MRI findings and SE prognosis in the acute phase.

Reduced-channel wearable electroencephalography (EEG) may overcome the accessibility and patient comfort limitations of traditional ambulatory electrographic seizure monitoring during extended-duration use. Automated algorithms are necessary for review of extended-duration reduced-channel EEG, yet current clinical support software is designed only for full-montage recordings.

The performance of a novel automated seizure detection algorithm for reduced-channel EEG (Epitel) was evaluated in a clinical validation study involving 50 participants (31 with seizures) with diverse demographic and seizure representation.

The algorithm demonstrated an event-level sensitivity of 86.2% (95% confidence interval [CI] = 79.5%-93.2%) and a false detection rate of .162 per hour (95% CI = .116-.221), which is comparable to the performance of current clinical software for full-montage EEG. Performance varied by electrographic seizure type, with 91.4% sensitivity for focal evolving to generalized seizures, 86.7% for generalized seizures, and 77.3% for focal seizures. The algorithm maintained robust performance in both pediatric participants aged 6-21 years (83% sensitivity) and adults aged 22+ years (90% sensitivity), as well as in ambulatory (80%) and epilepsy monitoring unit (EMU) monitoring environments (87.5%). The false detection rate in ambulatory monitoring environments (.290 false positive [FP] detections/h), all of which involved pediatric participants, was notably higher than in the EMU (.136 FP/h), indicating an area with clear need for improvement for unrestricted at-home monitoring. The algorithm's supplemental Confidence metric, designed to engender trust in the algorithm, showed a strong correlation with detection precision.

These results suggest that this algorithm can provide crucial support for review of extended-duration reduced-channel wearable EEG, enabling electrographic seizure monitoring with no restrictions on a person's daily life.

Autism is a severe neurodevelopmental disorder that affects many individuals around the world, with a constantly increasing prevalence. The association between autism and electroencephalographic (EEG) abnormalities in children suggests a worse evolution of clinical features. A retrospective study was conducted, including 101 children with autism who underwent clinical and neurological examination and wake electroencephalography. This study aimed to examine EEG abnormalities in children with autism, identify phenotypic characteristics associated with these abnormalities, asses their clinical relevance, and determine potential phenotypic correlations. The results showed that 10.89% of the patients in the study presented EEG abnormalities, similar to those of other studies that used wake EEG. Of these patients, 18.18% presented epileptic-type discharges, such as spike and wave complexes, and 81.81% presented non-epileptic-type abnormalities, such as bursts of slow waves, generalized or focal. Regarding the phenotypic profile of the patients with EEG abnormalities, 45.45% had a positive family history, 63.63% presented with dysmorphic features and 27.27% presented with gait disturbance. This study shows that some children with autism present multiple EEG abnormalities and diverse phenotypic traits in terms of personal and family history, dysmorphic features, and neurological examination. Identifying EEG abnormalities can improve clinical decisions with complex treatment and monitoring of co-occurring conditions like epilepsy. The use of accessible, effective, and noninvasive assessment tools, such as EEG recordings and neurological examinations in children with autism, can provide valuable support for improved case management.

BackgroundMost patients with idiopathic generalized epilepsy have good seizure control on antiseizure medications. Although idiopathic generalized epilepsy subtypes such as juvenile absence epilepsy and juvenile myoclonic epilepsy have a high risk of relapse, childhood absence epilepsy may have seizure remission. After 2 years of seizure freedom in childhood absence epilepsy, typically antiseizure medications are discontinued, but follow-up protocols are unclear. This study aims to evaluate how often patients with idiopathic generalized epilepsy undergo electroencephalography (EEG) after antiseizure medication withdrawal, how often antiseizure medications are restarted based on EEG findings, and if this varies between physicians and advanced practice providers at our institution.MethodsThis was a retrospective chart review. Data were collected using electronic medical records of pediatric patients (<18 years) with idiopathic generalized epilepsy who were successfully weaned off antiseizure medications at Penn State Children's Hospital from 2010 to 2020.ResultsWe reviewed 1409 charts and found 52 patients meeting criteria. Seventeen of 52 patients (32%) had a repeat EEG within 6 months of antiseizure medication withdrawal following seizure freedom. Of those 17 patients, 3 (17.6%) had generalized epileptiform discharges on EEG. Of these 3 patients, 2 (66%) were restarted on antiseizure medications based on the abnormal EEG. None had seizure relapse.ConclusionObtaining a repeat EEG in patients after antiseizure medication withdrawal following seizure freedom is common. Patients with an abnormal EEG are often restarted on antiseizure medications, irrespective of clinical seizure relapse. Considering the high health care costs of EEGs and antiseizure medication side effects, we propose that if patients with idiopathic generalized epilepsy do well clinically following antiseizure medication withdrawal, EEGs may not be necessary.

Glioma-associated epilepsy (GAE) is a common neurological symptom in glioma patients, which can worsen the condition and increase the risk of death on the basis of primary injury. Given this, accurate prediction of GAE is crucial, and this study aims to develop and validate a GAE warning recognition prediction model.

We retrospectively collected MRI scan imaging data and urine samples from 566 glioma patients at the Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science from August 2016 to December 2023. Least Absolute Shrinkage and Selection Operator (LASSO) regression and multivariate logistic regression analysis are used to determine independent risk factors for GAE. The nomogram and decision tree GAE visualization prediction model were constructed based on independent risk factors. The discrimination, calibration, and clinical usefulness of GAE prediction models were evaluated through receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA), respectively.

In the training and validation datasets, the incidence of GAE was 34.50% and 33.00%, respectively. Nomogram and decision tree were composed of five independent radiomic predictors and three differential protein molecules derived from urine. The discrimination rate of area under the curve (AUC) was 0.897 (95% CI: 0.840-0.954), slightly decreased in the validation data set, reaching 0.874 (95% CI: 8.817-0.931). The calibration curve showed a high degree of consistency between the predicted GAE probability and the actual probability. In addition, DCA analysis showed that in machine learning prediction models, decision trees have higher overall net returns within the threshold probability range.

We have introduced a machine learning prediction model for GAE detection in glioma patients based on multiomics data. This model can improve the prognosis of GAE by providing early warnings and actionable feedback and prevent or reduce pathological damage and neurobiochemical changes by implementing early interventions.

Scalp electroencephalography (EEG) is a cornerstone in the diagnosis and treatment of epilepsy, but routine EEG is often interpreted as normal without identification of epileptiform activity during expert visual review. The absence of interictal epileptiform activity on routine scalp EEGs can cause delays in receiving clinical treatment. These delays can be particularly problematic in the diagnosis and treatment of people with drug-resistant epilepsy (DRE) and those without structural abnormalities on MRI (i.e., MRI negative). Thus, there is a clinical need for alternative quantitative approaches that can inform diagnostic and treatment decisions when visual EEG review is inconclusive. In this study, we leverage a large population-level routine EEG database of people with and without focal epilepsy to investigate whether normal interictal EEG segments contain subtle deviations that could support the diagnosis of focal epilepsy.

We identified multiple epochs representing eyes-closed wakefulness from 19-channel routine EEGs of a large and diverse neurological patient population (N=13,652 recordings, 12,134 unique patients). We then extracted the average spectral power and phase-lag-index-based connectivity within 1-45Hz of each EEG recording using these identified epochs. We decomposed the power spectral density and phase-based connectivity information of all the visually reviewed normal EEGs (N=6,242) using unsupervised tensor decompositions to extract dominant patterns of spectral power and scalp connectivity. We also identified an independent set of routine EEGs of a cohort of patients with focal epilepsy (N= 121) with various diagnostic classifications, including focal epilepsy origin (temporal, frontal), MRI (lesional, non-lesional), and response to anti-seizure medications (responsive vs. drug-resistant epilepsy). We analyzed visually normal interictal epochs from the EEGs using the power-spectral and phase-based connectivity patterns identified above and evaluated their potential in clinically relevant binary classifications.

We obtained six patterns with distinct interpretable spatio-spectral signatures corresponding to putative aperiodic, oscillatory, and artifactual activity recorded on the EEG. The loadings for these patterns showed associations with patient age and expert-assigned grades of EEG abnormality. Further analysis using a physiologically relevant subset of these loadings differentiated patients with focal epilepsy from controls without history of focal epilepsy (mean AUC 0.78) but were unable to differentiate between frontal or temporal lobe epilepsy. In temporal lobe epilepsy, loadings of the power spectral patterns best differentiated drug-resistant epilepsy from drug-responsive epilepsy (mean AUC 0.73), as well as lesional epilepsy from non-lesional epilepsy (mean AUC 0.67), albeit with high variability across patients.

Our findings from a large population sample of EEGs suggest that normal interictal EEGs of patients with epilepsy contain subtle differences of predictive value that may improve the overall diagnostic yield of routine and prolonged EEGs. The presented approach for analyzing normal EEGs has the capacity to differentiate several diagnostic classifications of epilepsy, and can quantitatively characterize EEG activity in a scalable, expert-interpretable, and patient-specific fashion. Further technical development and clinical validation may yield normal EEG-derived computational biomarkers that could augment epilepsy diagnosis and assist clinical decision-making in the future.

Brain network analysis is an emerging field of research that could lead to the development, testing and validation of novel biomarkers for epilepsy. This could shorten the diagnostic uncertainty period, improve treatment, decrease seizure risk and lead to better management. This scoping review summarises the current state of electroencephalogram (EEG)-based network abnormalities for childhood epilepsies. The review assesses the overall robustness, potential generalisability, strengths, and limitations of the methodological frameworks of the identified research studies.

PRISMA guidelines for Scoping Reviews and the PICO framework was used to guide this review. Studies that evaluated candidate network-based features from EEG in children were retrieved from four international indexing databases (Cochrane Central / Embase / MEDLINE/ PsycINFO). Each selected study design, intervention characteristics, methodological design, potential limitations, and key findings were analysed.

Of 2,959 studies retrieved, nine were included. Studies used a group-level based comparison (e.g. based on a statistical test) or a classification-based method (e.g. based on a statistical model, such as a decision tree). A common limitation was the small sample-sizes (limiting further subgroup or confounder analysis) and the overall heterogeneity in epilepsy syndromes and age groups.

The heterogeneity of included studies (e.g. study design, statistical framework, outcome metrics) highlights the need for future studies to adhere to standardised frameworks (e.g. STARD) in order to develop standardised and robust methodologies. This would enable rigorous comparisons between studies, which is critical in assessing the potential of network-based approaches in developing novel biomarkers for childhood epilepsies.

Focal seizures may encompass vestibular sensations in their symptomatology. When these manifestations occur in isolation or constitute the predominant symptom, they prompt consideration for diagnosing recurrent paroxysmal vertigo. However, the characterization of "vestibular epilepsy" remains debated and underexplored. Our objective is to characterize the clinical and electrophysiological criteria of vestibular epilepsy.

We retrospectively analyzed data from a cohort of outpatients treated in the epileptology department of Marseille University Hospital. The study focused on patients presenting with vestibular symptoms without focal abnormalities on brain MRI, and with interictal epileptic abnormalities on wake or sleep EEG.

31 patients (15 men and 16 women) were included in the study. Visual, auditory, and dysautonomic symptoms were frequently associated with vestibular symptoms. The mean time to diagnosis was 3 years. The duration of attacks was generally short, ranging from a few seconds to a few minutes, with variable frequency. Most patients responded well to antiseizure medication. Some patients showed interictal phenomena, such as permanent instability, raising the possibility of inter/postictal disturbances. Seizures could be triggered by peripheral vestibular stimuli. Interictal EEG abnormalities were observed only during sleep in 25% of patients and predominated in the posterior temporoparietal regions.

We propose clinical-electro-radiological criteria for defining vestibular epilepsy. These diagnostic criteria overlap with the criteria for vestibular paroxysmia, suggesting the possibility of a single nosological entity.

Flexible electronics are integral in numerous domains such as wearables, healthcare, physiological monitoring, human-machine interface, and environmental sensing, owing to their inherent flexibility, stretchability, lightweight construction, and low profile. These systems seamlessly conform to curvilinear surfaces, including skin, organs, plants, robots, and marine species, facilitating optimal contact. This capability enables flexible electronic systems to enhance or even supplant the utilization of cumbersome instrumentation across a broad range of monitoring and actuation tasks. Consequently, significant progress has been realized in the development of flexible electronic systems. This study begins by examining the key components of standalone flexible electronic systems-sensors, front-end circuitry, data management, power management and actuators. The next section explores different integration strategies for flexible electronic systems as well as their recent advancements. Flexible hybrid electronics, which is currently the most widely used strategy, is first reviewed to assess their characteristics and applications. Subsequently, transformational electronics, which achieves compact and high-density system integration by leveraging heterogeneous integration of bare-die components, is highlighted as the next era of flexible electronic systems. Finally, the study concludes by suggesting future research directions and outlining critical considerations and challenges for developing and miniaturizing fully integrated standalone flexible electronic systems.

Evidence of the effects of chronic caffeine (CAFF)-containing beverages, alone or in combination with agomelatine (AGO) or quetiapine (QUET), on electroencephalography (EEG), which is relevant to cognition, epileptogenesis, and ovarian function, remains lacking. Estrogenic, adenosinergic, and melatonergic signaling is possibly linked to the dynamics of these substances.

The brain and ovarian effects of CAFF were compared with those of AGO + CAFF and QUET + CAFF. The implications of estrogenic, adenosinergic, and melatonergic signaling and the brain-ovarian crosstalk were investigated.

Adult female rats were administered AGO (10 mg/kg), QUET (10 mg/kg), CAFF, AGO + CAFF, or QUET + CAFF, once daily for 8 weeks. EEG, estrous cycle progression, and microstructure of the brain and ovaries were examined. Brain and ovarian 17β-estradiol (E2), antimullerian hormone (AMH), estrogen receptor alpha (E2Rα), adenosine receptor 2A (A2AR), and melatonin receptor 2 (MT2R) were assessed.

CAFF, alone or combined with AGO or QUET, reduced the maximum EEG peak, which was positively linked to ovarian E2Rα, negatively correlated to cortical neurodegeneration and ovarian MT2R, and associated with cystic ovaries. A large corpus luteum emerged with AGO + CAFF and QUET + CAFF, antagonizing the CAFF-mediated increased ovarian A2AR and reduced cortical E2Rα. AGO + CAFF provoked TTP delay and increased ovarian AMH, while QUET + CAFF slowed source EEG frequency to δ range and increased brain E2.

CAFF treatment triggered brain and ovarian derangements partially antagonized with concurrent AGO or QUET administration but with no overt affection of estrus cycle progression. Estrogenic, adenosinergic, and melatonergic signaling and brain-ovarian crosstalk may explain these effects.

To develop a low-cost portable EEG system, with real-time automated guidance, for application in resource-limited areas, to bridge the diagnostic and treatment gap.

We designed, developed, and produced a low-cost system, which records 27-channel EEG plus ECG and streams the signals to an application on a smartphone, which assesses the quality of the signal and gives feedback to the inexperienced user to correct the poor quality signals and reduce artifacts. The application guides the inexperienced user through the steps of recording routine clinical EEG. The recordings are uploaded to a secure cloud, for telemedicine applications. We recruited 10 participants without prior experience with recording EEG. After a brief training session, the participants recorded EEGs following the guidance from the app, without help from human experts. We assessed the usability of the system, with the System Usability Scale (SUS), and we evaluated the impedances and signal quality of the test EEGs recorded by the inexperienced users.

All users completed the test EEG recordings, and none of the recordings were of insufficient quality for clinical use. The SUS score was 90.3 ± 6.8, and the average quality rating was 8.04.

The low-cost, portable EEG system, which uses automated, real-time guidance for conducting EEG recordings, enables inexperienced users to record EEGs of a quality sufficient for clinical applications. This system has the potential to provide EEG services in resource-limited areas, and thereby help bridge the diagnostic and therapeutic gap.

Electroencephalography (EEG) is an important tool in the field of developmental cognitive neuroscience for indexing neural activity. However, racial biases persist in EEG research that limit the utility of this tool. One bias comes from the structure of EEG nets/caps that do not facilitate equitable data collection across hair textures and types. Recent efforts have improved EEG net/cap design, but these solutions can be time-intensive, reduce sensor density, and are more difficult to implement in younger populations. The present study focused on testing EEG sensor net designs over infancy. Specifically, we compared EEG data quality and retention between two high-density saline-based EEG sensor net designs from the same company (Magstim EGI, Whitland, UK) within the same infants during a baseline EEG paradigm. We found that within infants, the tall sensor nets resulted in lower impedances during collection, including lower impedances in the key online reference electrode for those with greater hair heights and resulted in a greater number of usable EEG channels and data segments retained during pre-processing. These results suggest that along with other best practices, the modified tall sensor net design is useful for improving data quality and retention in infant participants with curly or tightly-coiled hair.

Seizures and epilepsy are common neurologic conditions that are frequently encountered in the outpatient primary care setting. An accurate diagnosis relies on a thorough clinical history and evaluation. Understanding seizure semiology and classification is crucial in conducting the initial assessment. Knowledge of common seizure triggers and provoking factors can further guide diagnostic testing and initial management. The pharmacodynamic characteristics and side effect profiles of anti-seizure medications are important considerations when deciding treatment and counseling patients, particularly those with comorbidities and in special populations such as patient of childbearing potential.

Seizures should be diagnosed and treated to ensure optimal health outcomes in critically ill patients admitted in the medical intensive care unit (MICU). Continuous electroencephalography is still infrequently used in the MICU. We investigated the effectiveness of routine EEG (rEEG) in detecting seizures in the MICU. A total of 560 patients admitted to the MICU between October 2018 and March 2023 and who underwent rEEG were reviewed. Seizure-related rEEG constituted 47% of all rEEG studies. Totally, 39% of the patients experienced clinical seizures during hospitalization; among them, 48% experienced the seizure, and 13% experienced their first seizure after undergoing an rEEG study. Seventy-seven percent of the patients had unfavorable short-term outcomes. Patients with cardiovascular diseases were the most likely to have the suppression/burst suppression (SBS) EEG pattern and the highest mortality rate. The rhythmic and periodic patterns (RPPs) and electrographic seizure (ESz) EEG pattern were associated with seizures within 24 h after rEEG, which was also related to unfavorable outcomes. Significant predictors of death were age > 59 years, the male gender, the presence of cardiovascular disease, a Glasgow Coma Scale score ≤ 5, and the SBS EEG pattern, with a predictive performance of 0.737 for death. rEEG can help identify patients at higher risk of seizures. We recommend repeated rEEG in patients with ESz or RPP EEG patterns to enable a more effective monitoring of seizure activities.

To compare quantitative spectral parameters of visually-normal EEG between Mesial Temporal Lobe Epilepsy (MTLE) patients and healthy controls (HC).

We enrolled 26 MTLE patients and 26 HC. From each recording we calculated total power of all frequency bands and determined alpha-theta (ATR) and alpha-delta (ADR) power ratios in different brain regions. Group-wise differences between spectral parameters were investigated (p < 0.05). To test for associations between spectral-power and cognitive status, we evaluated correlations between neuropsychological tests and quantitative EEG (qEEG) metrics.

In all comparisons, ATR and ADR were significantly decreased in MTLE patients compared to HC, particularly over the hemisphere ipsilateral to epileptic activity. A positive correlation was seen in MTLE patients between ATR in ipsilateral temporal lobe, and results of neuropsychological tests of auditory verbal learning (RAVLT and RAVLT-D), short term verbal memory (Digit span backwards), and executive function (Weigl's sorting test). ADR values in the contralateral posterior region correlated positively with RAVLT-D and Digit span backwards tests.

Results confirmed that the power spectrum of qEEG is shifted towards lower frequencies in MTLE patients compared to HC.

Of note, our results were found in visually-normal recordings, providing further evidence of the value of qEEG for longitudinal monitoring of MTLE patients over time. Exploratory analysis of associations between qEEG and neuropsychological data suggest this could be useful for investigating effects of antiseizure medications on cognitive integrity in patients.

Interictal epileptiform discharges (IED) as large intermittent electrophysiological events are associated with various severe brain disorders. Automated IED detection has long been a challenging task, and mainstream methods largely focus on singling out IEDs from backgrounds from the perspective of waveform, leaving normal sharp transients/artifacts with similar waveforms almost unattended. An open issue still remains to accurately detect IED events that directly reflect the abnormalities in brain electrophysiological activities, minimizing the interference from irrelevant sharp transients with similar waveforms only. This study then proposes a dual-view learning framework (namely V2IED) to detect IED events from multi-channel EEG via aggregating features from the two phases: (1) Morphological Feature Learning: directly treating the EEG as a sequence with multiple channels, a 1D-CNN (Convolutional Neural Network) is applied to explicitly learning the deep morphological features; and (2) Spatial Feature Learning: viewing the EEG as a 3D tensor embedding channel topology, a CNN captures the spatial features at each sampling point followed by an LSTM (Long Short-Term Memories) to learn the evolution of these features. Experimental results from a public EEG dataset against the state-of-the-art counterparts indicate that: (1) compared with the existing optimal models, V2IED achieves a larger area under the receiver operating characteristic (ROC) curve in detecting IEDs from normal sharp transients with a 5.25% improvement in accuracy; (2) the introduction of spatial features improves performance by 2.4% in accuracy; and (3) V2IED also performs excellently in distinguishing IEDs from background signals especially benign variants.

Clinical electroencephalography (EEG) is a conservative medical field. This explains likely the significant gap between clinical practice and new research developments. This narrative review discusses possible causes of this discrepancy and how to circumvent them. More specifically, we summarize recent advances in three applications of clinical EEG: source imaging (ESI), high-frequency oscillations (HFOs) and EEG in critically ill patients.

Recently published studies on ESI provide further evidence for the accuracy and clinical utility of this method in the multimodal presurgical evaluation of patients with drug-resistant focal epilepsy, and opened new possibilities for further improvement of the accuracy. HFOs have received much attention as a novel biomarker in epilepsy. However, recent studies questioned their clinical utility at the level of individual patients. We discuss the impediments, show up possible solutions and highlight the perspectives of future research in this field. EEG in the ICU has been one of the major driving forces in the development of clinical EEG. We review the achievements and the limitations in this field.

This review will promote clinical implementation of recent advances in EEG, in the fields of ESI, HFOs and EEG in the intensive care.

Background. Sleep is an activation procedure and is considered the most potent and best-documented modulator of seizures and interictal epileptiform discharges (IEDs) on electroencephalogram (EEG). The precise role of sleep deprivation in the diagnostic process of epilepsy has not been fully clarified after more than 50 years of use. Sleep deprivation is a procedure that is accompanied by discomfort for patients and their families. Therefore, an accurate indication according to each patient-specific characteristic is needed. This study aims to assess the effectiveness of sleep deprivation EEG in the diagnostic process of patients with suspected epilepsy in our center. Methods. We included patients with a first unprovoked seizure and patients with paroxysmal events suspecting seizures who underwent a sleep deprivation EEG (sdEEG) or routine EEG (rEEG). All patients were subsequently classified with confirmed epilepsy or not. Results. We included 460 patients. The group with sdEEG consisted of 115 patients, while the group with rEEG comprised 345 patients. In the sdEEG group, 19 patients (17%) were confirmed with epilepsy, of which 17 presented interictal epileptiform discharges (IEDs). For the rEEG group, 66 patients (19%) were confirmed with epilepsy, of which 63 presented IEDs. The difference was not statistically significant. Conclusion. Our study failed to find a difference in the yield of sleep deprivation versus routine EEG in patients with epilepsy, but there are many significant confounders/sample biases that limit the generalizability of the findings, particularly to the majority of adult practices.

Epilepsy is a prevalent neurological disorder characterised by repetitive seizures. It is categorised into three types: generalised epilepsy (GE), focal epilepsy (FE), and combined generalised and focal epilepsy. Correctly subtyping the epilepsy is important to select appropriate treatments. The types are mainly determined (i.e., diagnosed) by their semiologies supported by clinical examinations, such as electroencephalography and magnetoencephalography (MEG). Although these examinations are traditionally based on visual inspections of interictal epileptic discharges (IEDs), which are not always visible, alternative analyses have been anticipated. We examined if resting-state brain activities can distinguish patients with GE, which would help us to diagnose the type of epilepsy.

The 5 min resting-state brain activities acquired using MEG were obtained retrospectively from 15 patients with GE. The cortical source of the activities was estimated at each frequency band from delta to high-frequency oscillation (HFO). These estimated activities were compared with reference datasets from 133 healthy individuals and control data from 29 patients with FE.

Patients with GE showed larger theta in the occipital, alpha in the left temporal, HFO in the rostral deep regions, and smaller HFO in the caudal ventral regions. Their area under the curves of the receiver operating characteristic curves was around 0.8-0.9. The distinctive pattern was not found for data from FE.

Patients with GE show distinctive resting-state brain activity, which could be a potential biomarker and used complementarily to classical analysis based on the visual inspection of IEDs.

Aims: Point-of-care electroencephalogram (POC-EEG) is an acute care bedside screening tool for the identification of nonconvulsive seizures (NCS) and nonconvulsive status epilepticus (NCSE). The objective of this narrative review is to describe the economic themes related to POC-EEG in the United States (US).Materials and methods: We examined peer-reviewed, published manuscripts on the economic findings of POC-EEG for bedside use in US hospitals, which included those found through targeted searches on PubMed and Google Scholar. Conference abstracts, gray literature offerings, frank advertisements, white papers, and studies conducted outside the US were excluded.Results: Twelve manuscripts were identified and reviewed; results were then grouped into four categories of economic evidence. First, POC-EEG usage was associated with clinical management amendments and antiseizure medication reductions. Second, POC-EEG was correlated with fewer unnecessary transfers to other facilities for monitoring and reduced hospital length of stay (LOS). Third, when identifying NCS or NCSE onsite, POC-EEG was associated with greater reimbursement in Medical Severity-Diagnosis Related Group coding. Fourth, POC-EEG may lower labor costs via decreasing after-hours requests to EEG technologists for conventional EEG (convEEG).Limitations: We conducted a narrative review, not a systematic review. The studies were observational and utilized one rapid circumferential headband system, which limited generalizability of the findings and indicated publication bias. Some sample sizes were small and hospital characteristics may not represent all US hospitals. POC-EEG studies in pediatric populations were also lacking. Ultimately, further research is justified.Conclusions: POC-EEG is a rapid screening tool for NCS and NCSE in critical care and emergency medicine with potential financial benefits through refining clinical management, reducing unnecessary patient transfers and hospital LOS, improving reimbursement, and mitigating burdens on healthcare staff and hospitals. Since POC-EEG has limitations (i.e. no video component and reduced montage), the studies asserted that it did not replace convEEG.

In the last century, 10-20 lead EEG recordings became the gold standard of surface EEG recordings, and the 10-20 system provided comparability between international studies. With the emergence of advanced EEG sensors, that may be able to record and process signals in much more compact units, this additional sensor technology now opens up opportunities to revisit current ambulatory EEG recording practices and specific patient populations, and even electrodes that are embedded into the head surface. Here, we aim to provide an overview of current limited sensor long-term EEG systems. We performed a literature review using Pubmed as a database and included the relevant articles. The review identified several systems for recording long-term ambulatory EEGs. In general, EEGs recorded with these modalities can be acquired in ambulatory and home settings, achieve good sensitivity with low false detection rates, are used for automatic seizure detection as well as seizure forecasting, and are well tolerated by patients, but each of them has advantages and disadvantages. Subcutaneous, subgaleal, and subscalp electrodes are minimally invasive and provide stable signals that can record ultra--long-term EEG and are in general less noisy than scalp EEG, but they have limited spatial coverage and require anesthesia, a surgical procedure and a trained surgeon to be placed. Behind and in the ear electrodes are discrete, unobtrusive with a good sensitivity mainly for temporal seizures but might miss extratemporal seizures, recordings could be obscured by muscle artifacts and bilateral ictal patterns might be difficult to register. Finally, recording systems using electrodes in a headband can be easily and quickly placed by the patient or caregiver, but have less spatial coverage and are more prone to movement because electrodes are not attached. Overall, limited EEG recording systems offer a promising opportunity to potentially record targeted EEG with focused indications for prolonged periods, but further validation work is needed.

Although electroencephalography (EEG) serves a critical role in the evaluation and management of seizure disorders, it is commonly misinterpreted, resulting in avoidable medical, social, and financial burdens to patients and health care systems. Overinterpretation of sharply contoured transient waveforms as being representative of interictal epileptiform abnormalities lies at the core of this problem. However, the magnitude of these errors is amplified by the high prevalence of paroxysmal events exhibited in clinical practice that compel investigation with EEG. Neurology training programs, which vary considerably both in the degree of exposure to EEG and the composition of EEG didactics, have not effectively addressed this widespread issue. Implementation of competency-based curricula in lieu of traditional educational approaches may enhance proficiency in EEG interpretation amongst general neurologists in the absence of formal subspecialty training. Efforts in this regard have led to the development of a systematic, high-fidelity approach to the interpretation of epileptiform discharges that is readily employable across medical centers. Additionally, machine learning techniques hold promise for accelerating accurate and reliable EEG interpretation, particularly in settings where subspecialty interpretive EEG services are not readily available. This review highlights common diagnostic errors in EEG interpretation, limitations in current educational paradigms, and initiatives aimed at resolving these challenges.

In this study, a deep learning model was established based on head MRI to predict a crucial evaluation parameter in the assessment of injuries resulting from human cytomegalovirus infection: the occurrence of glioma-related epilepsy. The relationship between glioma and epilepsy was investigated, which serves as a significant indicator of labor force impairment.

This study enrolled 142 glioma patients, including 127 from Shengjing Hospital of China Medical University, and 15 from the Second Affiliated Hospital of Dalian Medical University. T1 and T2 sequence images of patients' head MRIs were utilized to predict the occurrence of glioma-associated epilepsy. To validate the model's performance, the results of machine learning and deep learning models were compared. The machine learning model employed manually annotated texture features from tumor regions for modeling. On the other hand, the deep learning model utilized fused data consisting of tumor-containing T1 and T2 sequence images for modeling.

The neural network based on MobileNet_v3 performed the best, achieving an accuracy of 86.96% on the validation set and 75.89% on the test set. The performance of this neural network model significantly surpassed all the machine learning models, both on the validation and test sets.

In this study, we have developed a neural network utilizing head MRI, which can predict the likelihood of glioma-associated epilepsy in untreated glioma patients based on T1 and T2 sequence images. This advancement provides forensic support for the assessment of injuries related to human cytomegalovirus infection.

Electroencephalography is a method of detecting and analyzing electrical activity in the brain. This electrical activity can be recorded and processed to aid in the clinical diagnosis of mental disorders. In this study, a novel system for classifying schizophrenia patients from EEG recordings is presented. The developed algorithm decomposes the EEG signals into a system of radial basis functions using the method of fuzzy means. This decomposition helps to obtain the information from the various electrodes of the EEG and allows separating between healthy controls and patients with schizophrenia. The proposed method has been compared with classical machine learning algorithms, such as, K-Nearest Neighbor, Adaboost, Support Vector Machine, and Bayesian Linear Discriminant Analysis. The results show that the proposed method obtains the highest values in terms of balanced accuracy, recall, precision and F1 score, close to 93 % in all cases. The model developed in this study can be implemented in brain activity analysis systems that help in the prediction of patients with schizophrenia.

Cognitive dysfunction in epileptic patients is a high-incidence complication. Its mechanism is related to nervous system damage during seizures, but there is no effective diagnostic biomarker. Neuronal pentraxin 2 (NPTX2) is thought to play a vital role in neurotransmission and the maintenance of synaptic plasticity. This study explored how serum NPTX2 and electroencephalogram (EEG) slow wave/fast wave frequency ratio relate to cognitive dysfunction in patients with epilepsy.

To determine if serum NPTX2 could serve as a potential biomarker for diagnosing cognitive impairment in epilepsy patients.

The participants of this study, conducted from January 2020 to December 2021, comprised 74 epilepsy patients with normal cognitive function (normal group), 37 epilepsy patients with cognitive dysfunction [epilepsy patients with cognitive dysfunction (ECD) group] and 30 healthy people (control group). The mini-mental state examination (MMSE) scale was used to evaluate cognitive function. We determined serum NPTX2 levels using an enzyme-linked immunosorbent kit and calculated the signal value of EEG regions according to the EEG recording. Pearson correlation coefficient was used to analyze the correlation between serum NPTX2 and the MMSE score.

The serum NPTX2 level in the control group, normal group and ECD group were 240.00 ± 35.06 pg/mL, 235.80 ± 38.01 pg/mL and 193.80 ± 42.72 pg/mL, respectively. The MMSE score was lowest in the ECD group among the three, while no significant difference was observed between the control and normal groups. In epilepsy patients with cognitive dysfunction, NPTX2 level had a positive correlation with the MMSE score (r = 0.367, P = 0.0253) and a negative correlation with epilepsy duration (r = -0.443, P = 0.0061) and the EEG slow wave/fast wave frequency ratio value in the temporal region (r = -0.339, P = 0.039).

Serum NPTX2 was found to be related to cognitive dysfunction and the EEG slow wave/fast wave frequency ratio in patients with epilepsy. It is thus a potential biomarker for the diagnosis of cognitive impairment in patients with epilepsy.

The interplay between neurology and cardiology has gained significant attention in recent years, particularly regarding the shared pathophysiological mechanisms and clinical comorbidities observed in epilepsy and arrhythmias. Neuro-cardiac electrophysiology mapping involves the comprehensive assessment of both neural and cardiac electrical activity, aiming to unravel the intricate connections and potential cross-talk between the brain and the heart. The emergence of artificial intelligence (AI) has revolutionized the field by enabling the analysis of large-scale data sets, complex signal processing, and predictive modeling. AI algorithms have been applied to neuroimaging, electroencephalography (EEG), electrocardiography (ECG), and other diagnostic modalities to identify subtle patterns, classify disease subtypes, predict outcomes, and guide personalized treatment strategies. In this review, we highlight the potential clinical implications of neuro-cardiac mapping and AI in the management of epilepsy and arrhythmias. We address the challenges and limitations associated with these approaches, including data quality, interpretability, and ethical considerations. Further research and collaboration between neurologists, cardiologists, and AI experts are needed to fully unlock the potential of this interdisciplinary field.

The report of the electroencephalogram (EEG) results has traditionally been made using free-text formats with a huge variation in descriptions due to several factors. Recently, the International Federation of Clinical Neurophysiology (IFCN) endorsed the use of the Standardized Computer-based Organized Reporting of EEG (SCORE). This system has many advantages, but only some concerns have been investigated so far. This study aimed to assess the end-users acceptability of this proposed EEG report format. A 16-item electronic survey was sent to physicians who use EEG services of a medical diagnosis clinic. Physicians had been receiving the EEG reports in free-text formats from the same three board-certified electroencephalographers for the past three years. In January 2019, the report changed to the SCORE format. The survey assessed five main topics: physician information and historical use of EEG; personal preferences; comparative aspects of the formats; impact of the new format on clinical decision-making; and satisfaction. Thirty-two of 52 have responded to the survey (61%). On average, 81% of the responders have received enough reports with the new format to reliably complete the survey. Every responder prefers the standardized compared to the free-text format. Twenty-five responders like the inclusion of the head model, and interestingly, five suggest including another legend to differentiate "slow activity" from "other abnormal activity". Virtually all responders would recommend the new format, but one-third read only the conclusion. Our findings suggest high acceptability of this standardized report format. Despite the limitations of this study, we hope these findings contribute to the improvement and expansion of standardized EEG reporting systems.

The present study was designed to establish and evaluate an early prediction model of epilepsy after encephalitis in childhood based on electroencephalogram (ECG) and clinical features.

255 patients with encephalitis were randomly divided into training and verification sets and were divided into postencephalitic epilepsy (PE) and no postencephalitic epilepsy (no-PE) according to whether epilepsy occurred one year after discharge. Univariate and multivariate logistic regression analyses were used to screen the risk factors for PE. The identified risk factors were used to establish and verify a model.

This study included 255 patients with encephalitis, including 209 in the non-PE group and 46 in the PE group. Univariate and multiple logistic regression analysis showed that hemoglobin (OR = 0.968, 95% CI = 0.951-0.958), epilepsy frequency (OR = 0.968, 95% CI = 0.951-0.958), and ECG slow wave/fast wave frequency (S/F) in the occipital region were independent influencing factors for PE (P < 0.05).The prediction model is based on the above factors: -0.031 × hemoglobin -2.113 × epilepsy frequency + 7.836 × occipital region S/F + 1.595. In the training set and the validation set, the area under the ROC curve (AUC) of the model for the diagnosis of PE was 0.835 and 0.712, respectively.

The peripheral blood hemoglobin, the number of epileptic seizures in the acute stage of encephalitis, and EEG slow wave/fast wave frequencies can be used as predictors of epilepsy after encephalitis.

Epilepsy is a neurological disorder characterized by recurrent seizures affecting millions worldwide. Medically intractable seizures in epilepsy patients are not only detrimental to the quality of life but also pose a significant threat to their safety. Outcomes of epilepsy therapy can be improved by early detection and intervention during the interictal window period. Electroencephalography is the primary diagnostic tool for epilepsy, but accurate interpretation of seizure activity is challenging and highly time-consuming. Machine learning (ML) and deep learning (DL) algorithms enable us to analyze complex EEG data, which can not only help us diagnose but also locate epileptogenic zones and predict medical and surgical treatment outcomes. DL models such as convolutional neural networks (CNNs), inspired by visual processing, can be used to classify EEG activity. By applying preprocessing techniques, signal quality can be enhanced by denoising and artifact removal. DL can also be incorporated into the analysis of magnetic resonance imaging (MRI) data, which can help in the localization of epileptogenic zones in the brain. Proper detection of these zones can help in good neurosurgical outcomes. Recent advancements in DL have facilitated the implementation of these systems in neural implants and wearable devices, allowing for real-time seizure detection. This has the potential to transform the management of drug-refractory epilepsy. This review explores the application of ML and DL techniques to Electroencephalograms (EEGs), MRI, and wearable devices for epileptic seizure detection. This review briefly explains the fundamentals of both artificial intelligence (AI) and DL, highlighting these systems' potential advantages and undeniable limitations.

Epilepsy is a prevalent condition characterized by recurrent, unpredictable seizures. Monitoring with surface electroencephalography (EEG) is the gold standard for diagnosing epilepsy, but a time-consuming, uncomfortable and sometimes ineffective process for patients. Further, using EEG over a brief monitoring period has variable success, dependent on patient tolerance and seizure frequency. The availability of hospital resources and hardware and software specifications inherently restrict the options for comfortable, long-term data collection, resulting in limited data for training machine-learning models. This mini-review examines the current patient journey, providing an overview of the current state of EEG monitoring with reduced electrodes and automated channel reduction methods. Opportunities for improving data reliability through multi-modal data fusion are suggested. We assert the need for further research in electrode reduction to advance brain monitoring solutions towards portable, reliable devices that simultaneously offer patient comfort, perform ultra-long-term monitoring and expedite the diagnosis process.

Non-epileptic paroxysmal events (NEPEs) are common in pediatric patients and may be misdiagnosed as epileptic seizures. We aimed to study the distribution of NEPEs across age groups and with different comorbidities, and to correlate the patients' presenting symptoms with their final diagnosis after video-EEG.

We retrospectively analyzed video-EEG recordings of children aged one month to 18 years who were admitted between March 2005 and March 2020. Patients who experienced any NEPE while under video-EEG monitorization were evaluated in this study. Subjects with concomitant epilepsy were also included. The patients were first divided into 14 groups according to the basic characteristics of symptoms they reported at admission. The events captured on video-EEG were then classified into six NEPE categories based on the nature of the events. These groups were compared according to video-EEG results.

We retrospectively evaluated 1338 records of 1173 patients. The final diagnosis was non-epileptic paroxysmal event in 226 (19.3%) of 1173 patients. The mean age of the patients was 105.4 ± 64.4 months at the time of the monitoring. The presenting symptoms were motor in 149/226 (65.9%) patients, with jerking being the most common (n = 40, 17.7%). Based on video-EEG, the most common NEPE was psychogenic non-epileptic seizures (PNES) (n = 66, 29.2%), and the most common PNES subtype was major motor movements (n = 19/66, 28.8%). Movement disorders (n = 46, 20.4%) were the second most common NEPE and the most common NEPE (n = 21/60, 35%) in children with developmental delay (n = 60). Other common NEPEs were physiological motor movements during sleep (n = 33, 14.6%), normal behavioral events (n = 31, 13.7%), and sleep disorders (n = 15, 6.6%). Almost half of the patients had a prior diagnosis of epilepsy (n = 105, 46.5%). Following the diagnosis of NEPE, antiseizure medication (ASM) was discontinued in 56 (24.8%) patients.

Non-epileptiform paroxysmal events can be difficult to distinguish from epileptic seizures in children, especially in patients with developmental delay, epilepsy, abnormal interictal EEG, or abnormal MRI findings. Correct diagnosis of NEPEs by video-EEG prevents unnecessary ASM exposure in children and guides appropriate management of NEPEs.

We recently updated and merged the adapted clinical practice guidelines (CPGs) for the diagnosis and treatment of children with epilepsy of a tertiary-level hospital. Medical knowledge is always evolving. As a result, it is critical to revisit the clinical standards on a frequent basis to ensure that the best services are offered to the target receivers. The purpose of this article was to update and merge the CPGs at Alexandria University Children Hospital (AUCH) for the diagnosis (2014) and treatment (2016) of children with epilepsy to unify and standardize the practice for better care and outcome.

This review and update CPG project was initiated by assembling a Guideline Review Group (GRG). The GRG conducted focus group discussions and decided to search any published updates of the recommendations of the previously identified high-quality and evidence-based CPG developed by the SIGN (Scottish Intercollegiate Guidelines Network) and to merge the two previous local CPGs under one comprehensive CPG for full management of epilepsy in children. The high quality of the selected source CPG from SIGN was based on quality assessment of CPGs undertaken previously using the Appraisal of Guidelines for Research and Evaluation II Instrument. The GRG followed the Checklist for the Reporting of Updated Guidelines (CheckUp), which is the CPG tool recommended by the Enhancing the Quality and Transparency of health Research Network for reporting of updated CPGs in addition to the RIGHT-Ad@pt Checklist for Adapted CPGs. The finalized updated CPG draft was sent to the external reviewer group topic experts.

The group updated 10 main categories of recommendations from one source CPG (SIGN). The recommendations included (1) epilepsy diagnosis; (2) recognition, identification, and referral; (3) pharmacological treatment of epilepsy and epilepsy syndromes; (4) nonpharmacological treatment of epilepsy and epilepsy syndromes; (5) managing pharmacoresistant epilepsy; (6) management of epilepsy in special groups; (7) medications; (8) children and caregiver education and support; (9) comorbidities and mortality; and (10) transitional care from pediatric to adult care services.

The finalized CPG provides evidence-based guidance to health care providers in AUCH for the diagnosis and management of epilepsy in children. The study also established the significance of a collaborative clinical and methodological expert group for the update of CPGs, as well as the usability of the "CheckUp" and "RIGHT-Ad@pt" CPG Tools.

Epilepsy is an important cause of morbidity and mortality especially in low- and middle-income countries. People with epilepsy (PWE) face difficulties in access to healthcare, appropriate diagnostic tests and anti-seizure medications (ASM). Bhutan is one such country in the Himalayas that has reported doubling of the prevalence of epilepsy from 155.7 per 100,000 population in 2017 to 312.4 in 2021. The country has one centre for electroencephalography and magnetic resonance imaging for a population of 0.7 million and does not have any neurologists as of 2023. There are 16 ASMs registered in the country but only selected medications are available at the primary level hospitals (phenobarbital, phenytoin and diazepam). There are challenges in the availability of these medicines all time round the year in all levels of hospitals. Neurosurgical treatment options are limited by the lack of adequate pre-surgical evaluation facilities and lack of trained human resources. The majority of PWE have reported facing societal stigma with significant impact on the overall quality of life. It is important to screen for psychiatric comorbidities and provide psychological support wherever possible. There is a need for a comprehensive national guideline that will cater to the needs of PWE and their caregivers within the resources available in the country. A special focus on the institutional and human resource capacity development for the study and care of epilepsy is recommended.

This article provides recommendations on the minimum standards for recording routine ("standard") and sleep electroencephalography (EEG). The joint working group of the International Federation of Clinical Neurophysiology (IFCN) and the International League Against Epilepsy (ILAE) developed the standards according to the methodology suggested for epilepsy-related clinical practice guidelines by the Epilepsy Guidelines Working Group. We reviewed the published evidence using the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) statement. The quality of evidence for sleep induction methods was assessed by the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) method. A tool for Quality Assessment of Diagnostic Studies (QUADAS-2) was used to assess the risk of bias in technical and methodological studies. Where high-quality published evidence was lacking, we used modified Delphi technique to reach expert consensus. The GRADE system was used to formulate the recommendations. The quality of evidence was low or moderate. We formulated 16 consensus-based recommendations for minimum standards for recording routine and sleep EEG. The recommendations comprise the following aspects: indications, technical standards, recording duration, sleep induction, and provocative methods.