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The goal of epilepsy treatment is seizure freedom, typically with antiseizure medication (ASM). If patients fail to attain seizure control despite two trials of appropriately chosen ASMs at adequate doses, they are classified as having drug-resistant epilepsy (DRE). Adverse events (AEs) commonly occur in people with DRE because they are typically on ⩾2 ASMs, increasing the potential for drug–drug interactions. Early emerging AEs may impact adherence, decrease quality of life, and delay achieving optimal treatment dosages. Cenobamate is an oral ASM with a long half-life which has proven to be highly effective in clinical trials. An international Delphi panel of expert epileptologists experienced in the clinical use of cenobamate and other ASMs was convened to develop consensus best practices for managing patients during and after cenobamate titration, with consideration for its known pharmacokinetic and pharmacodynamic interactions, to allow patients to reach the most appropriate cenobamate dose while limiting tolerability issues. The modified Delphi process included one open-ended questionnaire and one virtual face-to-face meeting. Participants agreed that cenobamate can be prescribed for most patients experiencing focal-onset seizures. Patients initiating cenobamate therapy should have access to healthcare professionals as needed and their treatment response should be evaluated at the 100-mg dose. Patients with intellectual disabilities may need additional support to navigate the titration period. Proactive down-titration or withdrawal of sodium channel blockers (SCBs) is recommended when concomitant ASM regimens include ⩾2 SCBs. When applicable, maintaining a concomitant clobazam dose at ~5–10 mg may be beneficial. Patients taking oral contraceptives, newer oral anticoagulants, or HIV antiretroviral medications should be monitored for potential interactions. Because clinical evidence informing treatment decisions is limited, guidance regarding dose adjustments of non-ASM drugs was not developed beyond specific recommendations presented in the Summary of Product Characteristics. [ABSTRACT FROM AUTHOR]

Neonatal seizures are the most prevalent and distinctive sign of neurologic dysfunction in early-life. In spite the recent advances in medical care and technology in newborn intensive care units (NICU), it remains an important clinical issue of diagnosis, treatment, and prognosis. This was a retrospective, observational cohort study of neonates with seizures treated in the Ondokuz Mayıs University Faculty of Medicine NICU. Demographics of the babies, risk factors and etiology of seizure, type of clinical seizure, electroencephalographic and radiological findings, and anti-seizure treatments were recorded. The incidence of neonatal seizures was 4.5% in NICU admissions. Seventy-two babies with seizures included, 69,4% were diagnosed with electroclinical seizures. The most common seizure types were clonic (35.8%) and motor automatisms (32.8%). Perinatal asphyxia/hypoxic ischemic encephalopathy (HIE) (29.2%) was the most common etiological factor, whereas hypoglycemia was the most common metabolic problem (15.3%). Eighty-one percent of seizures due to HIE were observed in the first 48 h. Hyperbilirubinemia (kern icterus), hypocalcemia, and idiopathic neonatal convulsions were observed after the first 48 h. Abnormal findings were detected in 76.4% of electroencephalographies obtained during the neonatal period. Phenobarbital was the first-line therapy in 98.6% of babies, and 83.3% of the infants were seizure-free with phenobarbital. Seizures are common in the neonatal period and may be associated with significant brain damage. Seizures appear as an important symptom of the underlying pathology and not as a disease. [ABSTRACT FROM AUTHOR]

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Central nervous system (CNS) disorders, including seizures, status epilepticus (SE), and altered mental status, constitute a significant proportion of cases presenting in the pediatric emergency department. EEG is essential for diagnosing nonconvulsive SE, but standard EEG is often unavailable due to resource constraints. Point-of-care EEG (pocEEG) has emerged as a viable alternative, offering rapid bedside assessment. This systematic review synthesizes existing data on the use of pocEEG in pediatric emergencies and highlights research gaps. A comprehensive search of PubMed, CINAHL, and EMBASE identified six studies on pediatric populations using simplified EEG montages, with cohort sizes ranging from 20 to 242 patients. The findings indicate that pocEEG is feasible in acute pediatric care, effectively aiding in the detection of nonconvulsive SE and other critical neurological conditions. The studies varied in electrode placement strategies, ranging from neonatal to subhairline montages., Conclusion: Despite some implementation challenges, pocEEG has shown sufficient accuracy for clinical use. Further research should focus on optimizing EEG montages, refining interpretation, and assessing its impact on patient outcomes. This review underscores the potential of pocEEG to address critical care needs in pediatric emergency departments and calls for larger, standardized studies., What Is Known: •  Central nervous system (CNS) disorders, such as seizures and altered mental status, are common and critical conditions encountered in pediatric emergency resuscitation bays. •  EEG is essential for diagnosing nonconvulsive status epilepticus, but standard EEG is often unavailable in emergency departments due to logistical challenges, limited resources, and the need for specialized interpretation., What Is New: •  Reduced-lead, point-of-care EEG (pocEEG) is a feasible alternative for real-time bedside CNS monitoring in pediatric emergency settings, aiding in the diagnosis of nonconvulsive status epilepticus and guiding the management of convulsive status epilepticus. • This systematic review highlights the feasibility and clinical potential of pocEEG in pediatric emergency departments and identifies key areas for further research, including the development of standardized pocEEG protocols and the integration of automated EEG analysis., Competing Interests: Declarations. Ethics approval: This systematic review did not require ethical approval as it involved only the analysis of publicly available data from previously published studies. Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

Objective. To develop and evaluate a novel, non-patient-specific epileptic seizure prediction system using graded spiking neural networks (GSNNs) implemented on Intel's Loihi 2 neuromorphic processor, addressing the challenges of real-time, energy-efficient prediction to improve patient quality of life. Approach. The GSNN-based system utilized the CHB-MIT dataset for training, integrating hyperparameter optimization, electroencephalogram (EEG) channel selection for data reduction, and a multi-windowed voting mechanism for robustness against noise and artifacts. The system was deployed on Intel's Loihi 2 processor, leveraging its neuromorphic architecture for improved computational efficiency. Main results. The proposed system achieved a non-patient-specific prediction accuracy of 99.14%, outperforming traditional seizure prediction methods. The implementation achieved a throughput of 21.6 EEG segment inputs per second with an energy consumption of 25.104 mJ per input. Additionally, GSNN demonstrated a 6.26 times improvement in event sparsity and a 3.80 times improvement in synaptic communication sparsity compared to artificial neural networks. Significance. This study introduces a robust and energy-efficient GSNN-based framework for epileptic seizure prediction, significantly improving the potential for real-time, wearable applications. By enhancing efficiency and reducing computational complexity, the proposed system demonstrates the substantial promise of GSNNs in advancing neuromorphic computing and addressing critical challenges in epilepsy management., (© 2025 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.)

Objective . Epilepsy is a neurological disorder that affects millions of patients worldwide. Electroencephalogram-based seizure detection plays a crucial role in its timely diagnosis and effective monitoring. However, due to distribution shifts in patient data, existing seizure detection approaches are often patient-specific, which requires customized models for different patients. This paper considers privacy-preserving domain incremental learning (PP-DIL), where the model learns sequentially from each domain (patient) while only accessing the current domain data and previously trained models. This scenario has three main challenges: (1) catastrophic forgetting of previous domains, (2) privacy protection of previous domains, and (3) distribution shifts among domains. Approach . We propose a sparse knowledge sharing (SKS) approach. First, Euclidean alignment is employed to align data from different domains. Then, we propose an adaptive pruning approach for SKS to allocate subnet for each domain adaptively, allowing specific parameters to learn domain-specific knowledge while shared parameters to preserve knowledge from previous domains. Additionally, supervised contrastive learning is employed to enhance the model's ability to distinguish relevant features. Main Results . Experiments on two public seizure datasets demonstrated that SKS achieved superior performance in PP-DIL. Significance . SKS is a rehearsal-free privacy-preserving approach that effectively learns new domains while minimizing the impact on previously learned domains, achieving a better balance between plasticity and stability., (© 2025 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.)

Despite the vast number of publications reporting seizures and the reliance of the field on accurate seizure detection, there is a lack of open-source software tools in the scientific community for automating seizure detection based on electrographic recordings. Researchers instead rely on manual curation of seizure detection that is highly laborious, inefficient and can be error prone and heavily biased. Here we have developed - SeizyML - an open-source software that combines machine learning models with manual validation of detected events reducing bias and promoting efficient and accurate detection of electrographic seizures. We compared the validity of four interpretable machine learning classifiers (decision tree, gaussian naïve bayes, passive aggressive classifier, and stochastic gradient descent classifier) on an extensive electrographic seizure dataset that we collected from chronically epileptic mice. We find that the gaussian naïve bayes model detected all seizures in our dataset, had the lowest false detection rate, was robust to misclassifications, and only required a small amount of data to train. This approach has the potential to be a transformative research tool overcoming the analysis bottleneck that slows research progress., Competing Interests: Declarations. Competing Interests: J.M. serves on the Scientific Advisory Board for SAGE Therapeutics and Ovid Therapeutics. Information Sharing Statement: Information about figure reproduction related to model performance results is provided in the "Data and Code Availability" section. Please direct any requests regarding raw data sharing to the corresponding author., (© 2025. The Author(s).)

Electroencephalography (EEG) is a widely used physiological signal to obtain information of brain activity, and its automatic detection holds significant research importance, which saves doctors' time, improves detection efficiency and accuracy. However, current automatic detection studies face several challenges: large EEG data volumes require substantial time and space for data reading and model training; EEG's long-term dependencies test the temporal feature extraction capabilities of models; and the dynamic changes in brain activity and the non-Euclidean spatial structure between electrodes complicate the acquisition of spatial information. The proposed method uses range-EEG (rEEG) to extract time-frequency features from EEG to reduce data volume and resource consumption. Additionally, the next-generation state-space model Mamba is utilized as a temporal feature extractor to effectively capture the temporal information in EEG data. To address the limitations of state space models (SSMs) in spatial feature extraction, Mamba is combined with Dynamic Graph Neural Networks, creating an efficient model called DG-Mamba for EEG event detection. Testing on seizure detection and sleep stage classification tasks showed that the proposed method improved training speed by 10 times and reduced memory usage to less than one-seventh of the original data while maintaining superior performance. On the TUSZ dataset, DG-Mamba achieved an AUROC of 0.931 for seizure detection and in the sleep stage classification task, the proposed model surpassed all baselines.

Status epilepticus is a time-sensitive neuro-emergency, linked to poor functional outcomes and higher mortality rates. Prompt diagnosis and treatment are crucial to reduce its morbidity and mortality. Status epilepticus is often underdiagnosed in acutely ill hospitalized patients with altered consciousness, in whom most ongoing seizures can be subtle or nonconvulsive. For unexplained, persistent altered consciousness, clinicians should use electroencephalography to confirm or exclude a diagnosis of status epilepticus. A standardized treatment protocol should include prompt and adequately dosed first benzodiazepines as line therapy. Treatment approaches for second-line and third-line management continue to evolve as new anti-seizure medications become available., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Background and Objective: Catamenial seizure exacerbation (CSE) is challenging to track given unreliable patient reports. This highlights the need for improved recognition of CSE. In this study, we discuss the feasibility of using the Seizure Cycle app for that purpose., Design/methods: Eligible participants logged menstrual cycles and seizure data in the app for 6 months. CSE was defined based on criteria by Herzog et al [1], as two-fold increase in average daily seizure frequency (ADSF) during menstrual (C1) and ovulatory (C2) phases during ovulatory cycles, and the entire luteal phase during anovulatory cycles (C3). Feasibility was assessed by the proportion of participants who completed 4-month and 6-month documentation., Results: Among 8 participants, 5 (62.5 %) shared > 4-month data and 4 (50 %) shared 6-month data. Among the 6 participants who shared at least one month of data, CSE type C3 was identified based on number of seizures in one participant who had variable cycle length and was presumed to have anovulatory cycles. This was not confirmed with calculation of ADSF., Conclusions: Seizure Cycle app can serve as a feasible tool to improve diagnosis of this underrecognized condition. Despite the small sample size, CSE was potentially identified in one participant, although use of ADSF did not confirm this classification. Clearer definition of the C3 pattern may be useful. Future work will prioritize app automation to streamline data collection, facilitating larger and more robust datasets. These advancements will ultimately support the systematic assessment of therapeutic interventions to improve the diagnosis and treatment of CSE., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2025 Elsevier Inc. All rights reserved.)

Background: Visual changes due to hyperglycemia in diabetes are not uncommon. While blurred vision is a well-established sequela of chronic hyperglycemia, homonymous hemianopia with or without electroclinical seizures is much rarer and can be mistaken for migraine, temporal arteritis, or ischemia of the central nervous system., Methods: This article analyzed case studies for 3 patients (67M, 68M, 52F) presenting with complex visual phenomena, from 3 to 42 days duration, including pathogenesis, clinical findings, management, and follow-up., Results: Examinations demonstrated dense left homonymous hemianopias in 2 patients and a left inferior homonymous quadrantanopia in one, with no other abnormalities. Patients described vivid, nonstereotyped intermittent hallucinations in the affected fields. Blood glucose levels ranged from 13.5 to 35.0 mmol/L (243-630 mg/dL) without ketosis and HbA1c from 14.6% to 16.8%. Computed tomography of the brain showed no acute intracranial pathology. MRI of the brain either detected no abnormalities or demonstrated changes consistent with seizure activity. Electroencephalogram (EEG) demonstrated seizures over the right occipital region in each patient. EEG seizures coincided with patients' hallucinations, while they remained otherwise conscious. Oral hypoglycemic and antiepileptic medications were commenced with rapid and complete reversal of the seizures and visual field deficits, confirmed by repeat Automated 30-2 and MRI., Conclusions: Hyperglycemia-induced occipital lobe seizures with visual hallucinations and interictal homonymous visual field defects represent a rare but clinically important diagnosis. This article highlights the importance of prompt recognition and treatment to facilitate recovery., Competing Interests: M. T. Coroneo reports ongoing relationships with Katena/Corza and Allgenesis, and a past relationship with ASCRS. The remaining authors report no conflicts of interest., (Copyright © 2024 by North American Neuro-Ophthalmology Society.)

Rationale: Patients who experience seizures, including PNES, are usually advised to discontinue driving, or have their driving privileges revoked until a determined period of seizure-freedom is achieved. In this retrospective study, patients with PNES who requested driving privileges or reported having resumed driving were compared to those who did not on measures of depression, anxiety, PTSD, and cognitive flexibility/motor speed., Methods: DiagnosisofPNESwasconfirmedwithvideo-EEG.Demographicand clinical dataand requests for reinstatement of driving privileges (requiring 6 or more months seizure freedom) and reports of decisions to resume driving were noted. Tests of motor speed and hand eye coordination and self-report questionnaires of depression, anxiety and PTSD administered as part of neuropsychological assessment were analyzed., Results: A total of 403 patients with PNES evaluated in 2010-2020 were identified. Of those, 365 patients were eligible for inclusion, and of those, 60 applied for driving privileges or reported that they resumed driving. When the two groups were compared, the group that applied for driving privileges or decided to resume driving was significantly less depressed (p = 0.001) when tested than the group that did not. Furthermore, a significant difference was seen in measures of motor performance between those who requested to resume driving and those who did not (DKEFS T1, p = 0.006, DKEFS T2, p = 0.001, DKEFS T3, p = 0.002, DKEFS T4, p = 0.001; GPT dominant, p = 0.05, GPT non-dominant, p = 0.003)., Conclusion: Driving a motor vehicle is a useful measure of improvement for PNES because patients with seizures are required to discontinue driving until seizure-freedom is achieved. This study revealed that lower levels of depression and better fine motor functioning were associated with reported seizure-freedom and driving resumption. Depression is commonly associated with diminished performance (slower motor response times and impaired fine motor movements) on tests of motor functioning, both of which may result in less interest in pursuing permission to resume driving. These findings suggest that mood symptoms (and associated performance on measures of motor speed and coordination) may have prognostic significance in patients diagnosed with PNES. This also suggests that timely treatment of depression in newly diagnosed patients with PNES may be indicated., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2025 The Authors. Published by Elsevier Inc. All rights reserved.)

Objectives: To evaluate the availability and consistency of commercial driving eligibility criteria for patients with seizures., Methods: We systematically evaluated commercial driver's license regulations for patients with epilepsy, first acute symptomatic seizure and first unprovoked seizure in different countries. Government driving authority websites and published guidelines were accessed and if not available, local neurologists were contacted., Results: Information on commercial driving eligibility was available for 112 countries: 85 (76 %) via government websites or published guidelines and 27 (24 %) via direct contact with local neurologists. For epilepsy, 85 countries had clear guidelines: 42 countries requiring a seizure-free period of between 5 and 10 years, 43 applying a lifetime ban. Twenty-seven responding countries had no guidelines. For first acute symptomatic seizure (information available for 101 countries), 33 countries either required an individualised assessment or specified a seizure-free period ranging between 6 months and 10 years, 38 had a lifetime ban and 30 had no guidelines. For first unprovoked seizure (information available for 103 countries) 35 countries required seizure freedom for 1 to 10 years, 38 enforced a lifetime ban and 30 had no guidelines. There was considerable variation in the requirements for MRI, EEG, treatment with anti-seizure medication, and/or neurologist input., Conclusions: A marked lack of uniformity in commercial vehicle license guidelines exists for patients with seizures, likely in part reflecting the paucity of long-term data to inform evidence-based policy., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Crown Copyright © 2025. Published by Elsevier Inc. All rights reserved.)

Summary: The lack of reliable seizure detection remains a significant challenge for epilepsy care. A clinical deep brain stimulation (DBS) system provides constrained ambulatory brain recordings; however, limited data exist on the use of DBS recordings for seizure detection and lateralization. We present the case of an 18-year-old patient with drug-resistant focal epilepsy, who had seizure detection and lateralization by DBS recordings. Prior stereotactic-EEG, including a thalamus lead, identified independent left orbitofrontal and mesial temporal onset seizures. Notably, low-frequency thalamic ictal power was significantly elevated relative to baseline awake and sleep states. The patient was subsequently implanted with an anterior nucleus of the thalamus DBS system. Postimplantation, low-frequency power-in-band (5.3-10.3 Hz) recordings were initiated. Nursing staff identified four typical clinical seizures during the inpatient DBS recording period. Thalamic DBS trends contained relative peaks that were coincident with each nurse-reported seizure. Peri-ictal power was uniformly maximal ipsilateral to the seizure network. This case demonstrates the feasibility of seizure detection and lateralization by a thalamic DBS system for some individuals, and suggests DBS sensing parameter selection may be guided by thalamic stereotactic EEG. Further research is necessary to assess the generalizability of DBS seizure detection across individuals and diverse seizure networks., (Copyright © 2024 by the American Clinical Neurophysiology Society.)

Purpose: Late-onset epilepsy (LOE) usually refers to the development of epilepsy at the age of 50 years or older. Approximately 20 % of LOE cases are diagnosed as late-onset epilepsy of unknown etiology (LOUE) due to a lack of an identifiable cause. The aim of this study was to investigate the clinical features, seizure and cognitive outcomes of patients with LOUE in West China., Methods: Patients diagnosed with LOUE at West China Hospital between January 2015 and December 2022 were retrospectively recruited. The seizure and cognitive outcome were followed up for at least 1 year after discharge. Logistic regression models were applied to investigate the risk factors of recurrent seizure and cognitive impairment in patients with LOUE., Results: We included 286 LOUE patients with a median seizure onset age of 59 years. The most common seizure types were focal to bilateral tonic-clonic seizure (61.9 %) and focal non-motor seizure (37.0 %). Two-hundred and seventy-seven (96.9 %) patients underwent video electroencephalography (VEEG), with seizures recorded in 11.9 % of patients and interictal epileptiform discharges in 58.2 % cases. Majority of the patients (73.4 %) received monotherapy, with levetiracetam, oxcarbazepine and valproate being the most commonly prescribed anti-seizure medications. During the follow-up, 69.1 % of patients achieved seizure-free. Multivariate analysis identified ictal event recorded during VEEG monitoring (OR:0.205, 95 % CI: 0.045-0.932, p = 0.040) and memory impairment (OR:2. 470, 95 % CI: 1.181-5.167, p = 0.016) as significant factors associated with recurrent seizure. Twenty-two patients were classified as cognitive impairment. The onset age (OR:1.095, 95 % CI:1.032-1.162, p = 0.003) and total Fazekas score (OR = 6.770, 95 % CI:1.972-23.241, p = 0.002) were significant risk factors associated with cognitive dysfunction., Conclusion: LOUE is generally a benign form of epilepsy with a high percentage of patients achieving seizure-free status. However, these patients are at a higher risk of memory decline and cognitive dysfunction., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

The objective of this review is to present the role of specific interictal and ictal EEG onset patterns during scalp video-EEG monitoring. Specific non-epileptiform abnormalities include temporal intermittent rhythmic delta activity (TIRDA) and temporal intermittent rhythmic theta activity (TIRTA) indicating a temporal lobe seizure onset, while interictal rhythmical midline theta activity occurs more frequently in frontal epilepsy. Specific interictal epileptiform abnormalities comprise Type 1 spikes pointing towards a mesial and Type 2spikes indicating a lateral temporal irritative zone. Unilateral temporal interictal epileptiform discharges (IEDs) are predictive for a good surgical seizure outcome in temporal lobe epilepsy. Small sharp spikes (SSS) named Benign Epileptiform Transients of Sleep (BETS) in the past represent scalp EEG markers of hippocampal epileptic activity. While the localizing value of IEDs in extratemporal epilepsies is often limited, a consistently localized spike focus predicts a good surgical seizure outcome in non-lesional extratemporal patients. A specific ictal EEG pattern for mesial temporal lobe epilepsy consists of a 5-9 Hz rhythmic temporal activity which also predicts a good surgical outcome. In extratemporal epilepsies, ictal scalp EEG frequently is non-localized. Concerning the correspondence of ictal scalp-EEG and intracranial EEG (iEEG) patterns there is no simple one-to-one relationship. Scalp-EEG and iEEG patterns correspond closer to each other when there is no delay between clinical and scalp-EEG onset. Paroxysmal fast activity on scalp-EEG matches with low-voltage fast activity on iEEG. Repetitive epileptiform discharges on scalp EEG indicate an underlying focal cortical dysplasia., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2025 Elsevier Inc. All rights reserved.)

Despite numerous studies aimed at improving accuracy, the accurate prediction of epileptic seizures remains a challenge in clinical practice due to the high computational cost, poor real-time performance, and over-reliance on labelled data. To address these issues, a real-time seizure prediction method with spatio-temporal information transfer learning (RTSPM-STITL) has been proposed in this study. In the RTSPM-STITL method, the human brain is regarded as a time-varying high-dimensional neurodynamic system, in which epileptic seizures are viewed as state transitions caused by time-varying system parameters. Specifically, the spatio-temporal information transfer (STIT) model is firstly constructed by the recurrent neural network (RNN) and trained by the Force Learning (a real-time learning mechanism). Then the STIT model is utilized to transform the high-dimensional neurodynamic data into low-dimensional time series to capture the dynamic features of epileptic seizures. Also, the critical slowing down effect (CSD) of seizure dynamics is used to detect warning signals. The experimental results demonstrate that the proposed method can achieve higher accuracy and sensitivity without labeled data on both the CHB-MIT and Siena scalp EEG databases. Especially, the parameters of the STIT model can be updated in real-time based on patient data, without iterative training. More importantly, the STIT model can maintain high sensitivity and accuracy with only 48400 parameters, which is reduced by more than 91% compared with contrast models in this experiment. Therefore, the proposed method can significantly reduce the computational cost and accurately predict epileptic seizures, as well as with high real-time, practicality, applicability, and interpretability.

Objective: To evaluate whether abnormal sleep is associated with adverse outcomes for children who survived acute provoked neonatal seizures, and their parents., Study Design: This 9-center study prospectively followed newborns with acute provoked seizures. When children reached age 5 years, parents completed the Children's Sleep Habits Questionnaire (CSHQ), the Pediatric Sleep Questionnaire-Sleep Related Breathing Disorders (PSQ-SRBD) subscale, the Vineland Adaptive Behavior Scales-3, and the Hospital Anxiety Depression Scale. Children were also assessed with the Wechsler Preschool and Primary Scale of Intelligence-IV (WPPSI-IV). Spearman correlations and multivariable analyses were used to evaluate risk factors for sleep problems., Results: The mean CSHQ score was 45 ± 7; 77 of 118 children (65%) had an abnormal score (above the healthy sleep threshold of 41). On the PSQ-SRBD, 32 of 119 children (27%) screened positive for sleep-disordered breathing (SDB). SDB symptoms were more common among children with cerebral palsy (42% with vs 22% without; P = .03) and epilepsy (54% with vs 24% without; P = .02). Children with lower scores on the Vineland-3 (rho = -0.25; P = .01) and WPPSI-IV (rho = -0.31; P = .004) at 5 years of age were more likely to have symptoms of SDB. Worse CSHQ and PSQ-SRBD scores were associated with higher parental anxiety (rho = 0.28 [P = .002] and rho = 0.34 [P = .0002], respectively) and depression scores on the Hospital Anxiety Depression Scale (rho = 0.16 [P = .08] and rho = 0.17 [P = .07], respectively)., Conclusions: Two-thirds of early school-aged survivors of acute provoked neonatal seizures had parent-reported sleep abnormalities and one-quarter screened positive for SDB. Early screening and effective treatment for sleep disorders could be an innovative, practice-changing approach to improve outcomes after neonatal seizures., Competing Interests: Declaration of Competing Interest Renée Shellhaas reports financial support for this study was provided by National Institute of Neurological Disorders and Stroke and the Patient-Centered Outcomes Research Institute. Renée Shellhaas reports a relationship with Pediatric Epilepsy Research Foundation that includes: board membership and employment. Renée Shellhaas reports a relationship with The Epilepsy Study Consortium that includes: consulting or advisory. Renée Shellhaas reports a relationship with UptoDate Inc that includes: royalties for authorship of topics related to neonatal seizures. All other authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Objectives: Previous studies have identified features in patient's history and seizure descriptions supporting a clinical diagnosis of functional / dissociative seizures (FDS). However, most studies involved patients with chronic seizure disorders. This study explores the value of reported features for a clinical diagnosis of FDS in an adult population with a first presentation of transient loss of consciousness (TLoC)., Methods: We prospectively recruited patients newly presenting with TLoC to an Emergency Department (ED), Acute Medical Unit (AMU; admitting ward for general medical patients), first seizure or syncope clinic. We invited participants to complete an online questionnaire, either at home or at time of initial assessment. Two expert raters determined cause of participants' TLOC after 6-month follow-up. We also reviewed clinical records at this timepoint to extract relevant information for assessment of putative diagnostic features (13 categorical variables and 6 interval or continuous variables), and validation of two previously-developed diagnostic classifiers., Results: We included 178 patients in final analysis (134 syncope, 32 epilepsy, 12 FDS). 3 categorical variables were significantly more common in FDS: fluctuating course or waxing/waning movements (p = 0.0037), asynchronous limb movements (p = 0.0024), and preserved ictal awareness or responsiveness (p = 0.0013). Three interval/continuous variables supported diagnosis of FDS: younger age at onset (area under receiver-operating characteristic curve [AUC] = 0.865 (0.771-0.960)); total non-ictal symptoms reported on structured review of systems (AUC = 0.834 (0.730-0.928)); and total peri-ictal symptoms self-reported on structured questionnaire (AUC = 0.864 (0.781-0.948))., Conclusions: Our study does not find support for some clinical features previously reported as diagnostic of FDS in adult patients with a first presentation of TLoC. Features suggestive of preserved ictal responsiveness (reported by witnesses) and awareness (in the form of total number of self-reported peri-ictal symptoms) support FDS diagnoses., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2025 Elsevier Inc. All rights reserved.)

Introduction: Traumatic brain injury often requires neurologic care and specialized equipment, not often found downrange. Nonconvulsive seizures (NCSs) and nonconvulsive status epilepticus (NCSE) occur in up to 30% of patients with moderate or severe traumatic brain injury and is associated with a 39% morbidity and an 18% mortality. It remains difficult to identify at bedside because of the heterogeneous clinical manifestations. The primary diagnostic tool is an electroencephalogram (EEG) which is large, requires an external power source, and requires a specialized technician and neurologist to collect and interpret the data. Rapid response EEG (rr-EEG) is an FDA-approved device that is pocket sized and battery powered and uses a disposable 10-electrode headset. Prior studies have demonstrated the noninferiority of rr-EEG in the identification of NCSE and NCS as compared to conventional EEG in hospitals. An unanswered question is whether rr-EEG could be used in the identification of NCSE and NCS by medics., Materials and Methods: In conjunction with the Critical Care Air Transport (CCAT) team, a simulation was created and implemented on a CCAT training mission. The simulation team included a neurology resident, who oversaw the simulation, a pulmonary critical care fellow, an intensive care unit nurse, and a respiratory therapy. A survey was provided before and after the simulation. The team was expected to review the rr-EEG to make clinical decisions during ground transport, takeoff, and landing. The neurology resident monitored and recorded the team's ability to distinguish between NCS and a normal EEG. In between, the neurology resident monitored the quality of the EEG for potential interference and loss of quality., Results: The CCAT team was able to efficiently set up the rr-EEG on a patient manikin, correctly identify visual EEG wave forms of a patient in NCS, and utilize the proprietary audio program of a simulated patient in NCS. The team reported that the device was easily set up in the environment, and the data were interpretable despite vibration, aircraft auditory and electrical noise, and the ergonomics of the aircraft medical section., Conclusions: This pilot study has validated a potentially revolutionary technology in medical transport. The rr-EEG technology is measurably user-friendly and will improve patient outcomes. This device and simulation can reduce time to an EEG by hours to days allowing for immediate treatment and intervention, which can significantly reduce morbidity and mortality., (© The Association of Military Surgeons of the United States 2024. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site–for further information please contact journals.permissions@oup.com.)

While artificial intelligence has received considerable attention in various medical fields, its application in the field of electroconvulsive therapy (ECT) remains rather limited. With the advent of digital seizure collection systems, the development of novel ECT seizure quality metrics and treatment guidance systems in particular will require cutting-edge digital seizure analysis. Using artificial intelligence will offer more analytical degrees of freedom and could play a key role in enhancing the precision of currently available procedures. To this end, we developed the first machine learning (ML) framework that can classify ictal and non-ictal EEG segments, accurately identifying seizure endpoints-a critical step in deriving seizure quality parameters-and computing these metrics at least as reliable as existing precomputed scores. The ML model retained in this study effectively discriminated ictal from non-ictal EEG segments with 89% accuracy, precision, and sensitivity. The reproduced ECT quality parameters showed correlations up to ϱ = 0.99 (p < 0.01) with the pre-calculated values from the stimulation device and did not significantly differ from the reference values. Mean seizure duration differences were 0.23 ± 15.59 s compared to the expert rater and 0.28 ± 16.19 s compared to the stimulation device. The study highlights the potential of integrating ML into the field of ECT and emphasizes the critical role of a highly sensitive seizure detection method in reliably determining seizure duration and deriving subsequent quality indices, paving the way for more individualized treatment strategies and novel approaches to determine seizure quality., Competing Interests: Declarations. Competing interests: RH has received speaker or advisor honoraria from Atheneum, Boehringer Ingelheim, Janssen and Rovi. MK, MKi, AP and NF have no competing interests to declare. Ethics declarations: Due to the retrospective nature of this study of anonymized patient records, the Internal Review Board of the University Hospital Bonn waived the need of obtaining informed consent and approval. Data collection and methods were performed in accordance with local laws and regulations., (© 2025. The Author(s).)

Objective. The identification of spikes, as a typical characteristic wave of epilepsy, is crucial for diagnosing and locating the epileptogenic region. The traditional seizure detection methods lack spike features and have low sample richness. This paper proposes a seizure detection method with spike-based phase locking value (PLV) functional brain networks and multi-domain fused features. Approach. In the spiking detection part, brain functional networks based on PLV are constructed to explore the changes in brain functional states during spiking discharge, from the perspective of microscopic neuronal activity to macroscopic brain region interactions. Then, in the epilepsy seizure detection task, multi-domain fused feature sequences are constructed using time-domain, frequency-domain, inter-channel correlation, and the spike detection features. Finally, Bi-LSTM and Transformer encoders and their optimized models are used to verify the effectiveness of the proposed method. Main results. Experimental results achieve the best seizure detection metrics on Bi-LSTM-Attention, with accuracy, sensitivity, and specificity reaching 98.40%, 98.94%, and 97.86%, respectively. Significance. The method is significant as it innovatively applies multi channel spike network features to seizure detection. It can potentially improve the diagnosis and location of the epileptogenic region by accurately detecting seizures through the identification of spikes, which is a crucial characteristic wave of epilepsy., (© 2025 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.)

Epilepsy is a common neurological disorder in which abnormal brain waves propagate rapidly in the brain in the form of a graph network during seizures, and seizures are extremely sudden. So, designing accurate and reliable prediction methods can provide early warning for patients, which is crucial for improving their lives. In recent years, a large number of studies have been conducted using deep learning models on epileptic open electroencephalogram (EEG) datasets with good results, but due to individual differences there are still some subjects whose seizure features cannot be accurately captured and are more difficult to differentiate, with poor prediction results. Important time-varying information may be overlooked if only graph space features during seizures are considered. To address these issues, we propose a synchronization-based graph spatio-temporal attention network (SGSTAN). This model effectively leverages the intricate information embedded within EEG recordings through spatio-temporal correlations. Experimental results on public datasets demonstrate the efficacy of our approach. On the CHB-MIT dataset, our method achieves accuracy, specificity, and sensitivity scores of 98.2%, 98.07%, and 97.85%, respectively. In the case of challenging subjects that are difficult to classify, we achieved an outstanding average classification accuracy of 97.59%, surpassing the results of previous studies., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

Background: Electroencephalography (EEG) is needed to diagnose nonconvulsive seizures. Prolonged nonconvulsive seizures are associated with neuronal injuries and deleterious clinical outcomes. However, it is uncertain whether the rapid identification of these seizures using point-of-care EEG (POC-EEG) can have a positive impact on clinical outcomes., Methods: In a retrospective subanalysis of the recently completed multicenter Seizure Assessment and Forecasting with Efficient Rapid-EEG (SAFER-EEG) trial, we compared intensive care unit (ICU) length of stay (LOS), unfavorable functional outcome (modified Rankin Scale score ≥ 4), and time to EEG between adult patients receiving a US Food and Drug Administration-cleared POC-EEG (Ceribell, Inc.) and those receiving conventional EEG (conv-EEG). Patient records from January 2018 to June 2022 at three different academic centers were reviewed, focusing on EEG timing and clinical outcomes. Propensity score matching was applied using key clinical covariates to control for confounders. Medians and interquartile ranges (IQRs) were calculated for descriptive statistics. Nonparametric tests (Mann-Whitney U-test) were used for the continuous variables, and the χ 2 test was used for the proportions., Results: A total of 283 ICU patients (62 conv-EEG, 221 POC-EEG) were included. The two populations were matched using demographic and clinical characteristics. We found that the ICU LOS was significantly shorter in the POC-EEG cohort compared to the conv-EEG cohort (3.9 [IQR 1.9-8.8] vs. 8.0 [IQR 3.0-16.0] days, p = 0.003). Moreover, modified Rankin Scale functional outcomes were also different between the two EEG cohorts (p = 0.047)., Conclusions: This study reveals a significant association between early POC-EEG detection of nonconvulsive seizures and decreased ICU LOS. The POC-EEG differed from conv-EEG, demonstrating better functional outcomes compared with the latter in a matched analysis. These findings corroborate previous research advocating the benefit of early diagnosis of nonconvulsive seizure. The causal relationship between the type of EEG and metrics of interest, such as ICU LOS and functional/clinical outcomes, needs to be confirmed in future prospective randomized studies., Competing Interests: Conflicts of interest: The authors declare no conflicts of interest. Ethical Approval/Informed Consent: This study was approved by the local institutional review boards, and the requirement for informed consent was waived., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Objective: There are fewer reports on the ictal electroencephalogram(EEG) of convulsions in infants and children with mild gastroenteritis (BCWG). Our study retrospectively analyzed the ictal EEG characteristics of convulsive episodes of BCWG., Methods: The seizure-phase EEGs of children diagnosed with BCWG from September 2016 to January 2022 were searched and analyzed, and a total of thirteen seizure-phase EEGs of eight cases were analyzed retrospectively., Results: The age of onset of the disease in the eight children ranged from one year to two years and seven months. No epileptiform discharges were found during the interictal EEG. A total of thirteen epileptic seizures were monitored, of which nine were focal with secondary generalisation. Scalp EEG showed three onset of EEG seizures were in the right occipital region, two were in the right parietal and occipital regions, one was in the right parietal, occipital and posterior temporal region, one was in the right posterior temporal region, one was in the right occipital and posterior temporal region and one was in the right central and central midline region. In addition, four non-convulsive epileptic seizures, which manifested themselves as a decrease in movement and consciousness, with local δ slow-wave activity in bilateral occipital regions evolved to diffuse δ slow wave activity in epileptic seizures., Conclusion: Focal secondary generalised tonic clonic seizures were the main type of seizures in BCWG, and the seizure onset may been mostly in posterior cephalic cortex. It was previously unreported that some children of BCWG may combine with non-convulsive epileptic seizures with the onset of slow wave activity in the occipital region bilaterally and evolving into diffuse δ slow wave activity, which was not easily detected by parents and clinicians., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Objective: Seizure suppression using the neurofeedback (NFB) method by self-regulation of scalp-recorded slow cortical potential (SCP) is effective for patients with refractory focal epilepsy. However, the prolonged training period required prevents it from wider implementation as the standard treatment in clinical practice. Therefore, we examined whether it would be appropriate to shorten the training period, in spite of the small number of patients., Methods: 12 patients participated the NFB training. 1 course of NFB training consisted of 35 sessions divided into 2 phases. After each phase we evaluated whether each patient acquired NFB control, and seizure reduction was assessed compared to that before training., Results: Of 11 patients who completed the first training phase, 4 showed reduced post-training seizure frequency, of whom 3 could regulate the SCP polarity (NFB control). Of the remaining 7, 2 acquired NFB control during the second training phase but seizure frequency did not decrease. Furthermore, the other 5 did not acquire NFB control, and seizure frequency did not decrease., Conclusions: Patients who acquired NFB control within a short period, i.e., 3 weeks, were more likely to exhibit a lower post-training seizure frequency., Significance: SCP self-regulation can be acquired within a short period and is associated with seizure reduction., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.)

Transfer learning is one of the popular methods to solve the problem of insufficient data in subject-specific electroencephalogram (EEG) recognition tasks. However, most existing approaches ignore the difference between subjects and transfer the same feature representations from source domain to different target domains, resulting in poor transfer performance. To address this issue, we propose a novel subject-specific EEG recognition method named deep multiview module adaption transfer (DMV-MAT) network. First, we design a universal deep multiview (DMV) network to generate different types of discriminative features from multiple perspectives, which improves the generalization performance by extensive feature sets. Second, module adaption transfer (MAT) is designed to evaluate each module by the feature distributions of source and target samples, which can generate an optimal weight sharing strategy for each target subject and promote the model to learn domain-invariant and domain-specific features simultaneously. We conduct extensive experiments in two EEG recognition tasks, i.e., motor imagery (MI) and seizure prediction, on four datasets. Experimental results demonstrate that the proposed method achieves promising performance compared with the state-of-the-art methods, indicating a feasible solution for subject-specific EEG recognition tasks. Implementation codes are available at https://github.com/YangLibuaa/DMV-MAT.

Wearable devices for health monitoring are essential for tracking individuals' health status and facilitating early detection of diseases. However, processing biomedical signals online for real-time monitoring is challenging due to limited computational resources on edge devices. To address this challenge, we propose an application-agnostic methodology called ACE (Automated optimization towards classification on the Edge). ACE converts a health monitoring algorithm with feature extraction and classification into an iterative detection process, incorporating algorithms of varying complexities and minimizing re-computation of shared data. First, ACE decomposes a monolithic model, employing a single feature set and classifier, into multiple algorithms with different computational complexities. Then, our automatic analysis tool integrates buffering logic into these algorithms to prevent re-computation of shared computational-intensive data. The optimized algorithm is then converted into a low-level language in C for deployment. During runtime, the system initiates monitoring with the lowest complexity algorithm and iteratively involves algorithms with higher complexity without recomputing the existing data. The iteration process continues until a pre-defined confidence threshold is met. We demonstrate the effectiveness of ACE on two biomedical applications: seizure detection and emotional state classification. ACE achieves at least 28.9% and 18.9% runtime savings without any accuracy loss on a Cortex-A9 edge platform for the two benchmarks, respectively. We discuss and demonstrate how ACE can be used by designers of such biomedical algorithms to automatically optimize and deploy their applications on the edge.

Purpose: The responsive neurostimulation system (RNS) aims to improve seizures by delivering electrical stimulation in response to epileptiform patterns detected by electrocorticograms. Seizure-onset patterns (SOPs) correspond to outcomes in intracranial EEG (IC-EEG), although whether this is true for RNS is unknown. This study characterizes common RNS SOPs and correlates them with seizure outcomes., Methods: Among 40 patients with RNS implants, long-episode electrocorticogram characteristics of each patient's seizures were classified by visual analysis as one of the eight patterns previously described in IC-EEG. Correlation between each type of SOP and eventual seizure outcome was analyzed, with ≥50% improvement in a number of patient-reported seizure counts defined as a favorable outcome., Results: Across 263 LEs analyzed, the most common SOP observed was low-voltage fast activity. There was no difference between the distribution of RNS SOPs and that of IC-EEG SOPs described in the literature (Kolmogorov-Smirnov test, P = 0.98). Additionally, there was no correlation between any particular SOP and favorable outcomes (Fisher's omnibus test, P = 0.997)., Conclusion: This initial description of RNS SOPs finds them to be similar to previously described IC-EEG SOPs, which suggests similar prognostic/therapeutic potential. However, we found that RNS efficacy is independent of patient SOP, suggesting that RNS is likely an equally effective treatment for all SOPs. Future research on stimulation parameters for particular RNS SOPs and correlation with IC-EEG SOPs in the same patients would be instrumental in guiding personalized neurostimulation., Competing Interests: The authors have no funding or conflicts of interest to disclose. S. Cleboski was employed by NeuroPace, Inc during the study period. The remaining authors have no conflicts of interest to disclose., (Copyright © 2024 by the American Clinical Neurophysiology Society.)

Understanding how seizure semiology changes with age is essential to determine the seizure onset zone. Epilepsy can be considered the prototypical neurologic disorder for demonstrating age-related changes over time. The maturational changes that occur in the brain over the lifespan demonstrate themselves most clearly through semiologic changes. Due to the immaturity of the neonatal brain, seizure recognition is challenging. Electroclinical seizures are classified as motor, non-motor, sequential, or unclassified and are typically focal in onset. During infancy, the most common seizure types are epileptic spasms, myoclonic, tonic, atonic, clonic, and hypomotor/behavioral arrest seizures. Correlation between seizure semiology and localization of seizure onset zone can be variable. The most observed seizure types in preschool-aged children are generalized myoclonic, generalized tonic, focal tonic, or clonic seizures. Many of the epileptic encephalopathies present at this age. Ictal behaviors continue to be limited, but lateralizing motor manifestations during focal seizures are better developed compared to infants. In school-aged children, the most common seizure types involve change in awareness. Seizure semiology at this age resembles that of adults, with increasing number and complexity of seizure components., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Katherine Nickels, MD reports was provided by Mayo Clinic. Katherine Nickels, MD reports a relationship with Takeda Pharmaceutical Company Limited that includes: consulting or advisory. Katherine Nickels, MD reports a relationship with ApotheCom that includes: consulting or advisory. Katherine Nickels, MD reports a relationship with Biocodex USA that includes: consulting or advisory. Katherine Nickels, MD reports a relationship with Zogenix International Ltd that includes: consulting or advisory. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Epilepsy is a globally distributed chronic neurological disorder that may pose a threat to life without warning. Therefore, the use of wearable devices for real-time detection and treatment of epilepsy is crucial. Additionally, personalizing disease detection algorithms for individual users is also a challenge in clinical applications. Some studies have proposed seizure detection algorithms with convolutional neural networks (CNNs) and programmable hardware architectures for speeding up the process of CNN inference. However, personalizing seizure detection algorithms could still not be performed on these hardware architectures. Consequently, this study proposes three key contributions to address the challenges: a real-time seizure detection and personalization algorithm, a programmable reduced instruction set computer-V (RISC-V) deep learning accelerator (DLA) hardware architecture (RVDLAHA), and a dedicated RISC-V DLA (RVDLA) compiler. In animal experiments with lab rats, the proposed CNN-based seizure detection algorithm obtains an accuracy of 99.5% for a 32-bit floating point and an accuracy of 99.3% for a 16-bit fixed point. Additionally, the proposed personalization algorithm increases the testing accuracy across different databases from 85.0% to 92.9%. The RVDLAHA is implemented on Xilinx PYNQ-Z2, with a power consumption of only 0.107 W at an operating frequency of 1 MHz. Each step, including raw data input, preprocessing, detection, and personalization, requires only 17.8, 1.0, 1.1, and 1.3 ms, respectively. With the hardware architecture, the seizure detection and personalization algorithm can provide on-device real-time monitoring.

Neurologists often face challenges in identifying epileptic activities within multichannel EEG recordings, requiring extensive hours of analysis. Computer-aided diagnosis systems have been proposed to reduce manual inspection of EEG signals by neurologists. However, direct analysis of EEG signals is difficult due to their complex and dynamic nature, with variation across multiple patients. Therefore, researchers have proposed the short-time Fourier transform (STFT) to capture dynamic events indicative of seizures through time-varying frequency representation of EEG signals. However, tradeoffs between time and frequency resolution limited the spectrogram's interpretability and affected clinical deployment. Hence, this study proposes extracting high-resolution channels via a novel STFT spectrogram construction algorithm encompassing taper functions for seizure diagnosis. Initially, we extracted seizure and non-seizure segments from each channel of selected patients in the CHB-MIT dataset. Next, we systematically apply taper functions like Hann and Gaussian windows to minimize the edge effect during the construction of spectrogram images. Finally, we employ Dilated Convolutional Squeeze and Excitation Networks (DCSENets) through leave-one-patient-out cross-validation (LOPOCV) to perform patient-independent seizure classification. The proposed DCSENets achieve an average accuracy of 87.20±11.48% and 87.29±10.48% with Hann and Gaussian taper functions, respectively, and 86.85±11.56% without the taper function. Most patients with high performances indicate similarity in train-test sample distribution using the Kolmogorov-Smirnov test at 0.010.05. Furthermore, the Grad CAM deep visual explainer integration enhances the interpretability of the deep learning model's decision-making process. Consequently, neurologists are provided not only with enhanced visualized spectrograms but also a transparent model for improved seizure diagnosis., Competing Interests: Declaration of competing interest The authors declared that they have no conflicts of interest to this work. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Introduction: This study examines the usability and comfort of a behind-the-ear seizure detection device called brain seizure detection (BrainSD) that captures ictal electroencephalogram (EEG) data using four scalp electrodes., Methods: This is a feasibility study. Thirty-two patients admitted to a level 4 Epilepsy Monitoring Unit were enrolled. The subjects wore BrainSD and the standard 21-channel video-EEG simultaneously. Epileptologists analyzed the EEG signals collected by BrainSD and validated it using video-EEG data to confirm its accuracy. A poststudy survey was completed by each participant to evaluate the comfort and usability of the device. In addition, a focus group of UT Southwestern epileptologists was held to discuss the features they would like to see in a home EEG-based seizure detection device such as BrainSD., Results: In total, BrainSD captured 11 of the 14 seizures that occurred while the device was being worn. All 11 seizures captured on BrainSD had focal onset, with three becoming bilateral tonic-clonic and one seizure being of subclinical status. The device was worn for an average of 41 hours. The poststudy survey showed that most users found the device comfortable, easy-to-use, and stated they would be interested in using BrainSD. Epileptologists in the focus group expressed a similar interest in BrainSD., Conclusions: Brain seizure detection is able to detect EEG signals using four behind-the-ear electrodes. Its comfort, ease-of-use, and ability to detect numerous types of seizures make BrainSD an acceptable at-home EEG detection device from both the patient and provider perspective., Competing Interests: The authors have no funding or conflicts of interest to disclose., (Copyright © 2024 by the American Clinical Neurophysiology Society.)

Objective: To employ the neonatal seizure framework developed by the International League Against Epilepsy (ILAE) Neonatal Task force to assess its usefulness in determining the etiology of neonatal seizures., Methods: The members of the ILAE Neonatal Task Force evaluated 157 seizures from 146 neonates to determine internal validity and associations between semiology and a specific etiology., Results: Provoked neonatal electrographic and electroclinical seizures were due to multiple etiologies. For electroclinical seizures, unilateral clonic seizures were typically seen with vascular etiologies, focal tonic seizures and sequential seizures with genetic etiologies, and myoclonic seizures with inborn errors of metabolism. Electrographic seizures were often seen in hypoxic-ischemic encephalopathy or vascular etiologies., Significance: These data suggest that the ILAE neonatal seizure classification may be used as a bedside tool to aid and guide workup to determine the etiology of seizures., (© 2024 International League Against Epilepsy.)

Objective: Up to a third of patients with epilepsy fail to achieve satisfactory seizure control. A reliable method of predicting seizures would alleviate psychological and physical impact. Dysregulation in heart rate variability (HRV) has been found to precede epileptic seizures and may serve as an extracerebral predictive biomarker. This study aims to identify the preictal HRV dynamics and unveil the factors impeding the clinical application of ECG-based seizure prediction., Methods: Thirty-nine adult patients (eight women; median age: 38, [IQR = 31, 56.5]) with 252 seizures were included. Each patient had more than three recorded epileptic seizures, each at least 2 hours apart. For each seizure, one hour of ECG prior to seizure onset was analyzed and 97 HRV features were extracted from overlapping three-minute windows with 10s stride. Two separate patient-specific experiments were performed using a support vector machine (SVM). Firstly, the separability of training data was examined in a non-causal trial. Secondly, the prediction was attempted in pseudo-prospective conditions. Finally, visualized HRV data, clinical metadata, and results were correlated., Results: The mean receiver operating characteristic (ROC) area under the curve (AUC) for the non-causal experiment was 0.823 (±0.12), with 208 (82.5%) seizures achieving an improvement over chance (IoC) classification score (p < 0.05, Hanley & McNeil test). In pseudo-prospective classification, the ROC-AUC was 0.569 (±0.17), and 86 (49.4%) seizures were classified with IoC. Off-sample optimized SVMs failed to improve performance. Major limiting factors identified include non-stationarity, variable preictal duration and dynamics. The latter is expressed as both inter-seizure onset zone (SOZ) and intra-SOZ variability., Significance: The pseudo-prospective preictal classification achieving IoC in approximately half of tested seizures suggests the presence of genuine preictal HRV dynamics, but the overall performance does not warrant clinical application at present. The limiting factors identified are often overlooked in non-causal study designs. While current deterministic prediction methods prove inadequate, probabilistic approaches may offer a promising alternative., Plain Language Summary: Many patients with epilepsy suffer from uncontrollable seizures and would greatly benefit from a reliable seizure prediction method. Currently, no such system is available to meet this need. Previous studies suggest that changes in the electrocardiogram (ECG) precede seizures by several minutes. In our work, we evaluated whether variations in heart rate could be used to predict epileptic seizures. Our findings indicate that we are still far from achieving results suitable for clinical application and highlight several limiting factors of present seizure prediction approaches., (© 2024 The Author(s). Epilepsia Open published by Wiley Periodicals LLC on behalf of International League Against Epilepsy.)

Purpose: Electrographic seizures (ES) are common in critically ill children undergoing continuous EEG (CEEG) monitoring, and previous studies have aimed to target limited CEEG resources to children at highest risk of ES. However, previous studies have relied on observational data in which the duration of CEEG was clinically determined. Thus, the incidence of late occurring ES is unknown. The authors aimed to assess the incidence of ES for 24 hours after discontinuation of clinically indicated CEEG., Methods: This was a single-center prospective study of nonconsecutive children with acute encephalopathy in the pediatric intensive care unit who underwent 24 hours of extended research EEG after the end of clinical CEEG. The authors assessed whether there were new findings that affected clinical management during the extended research EEG, including new-onset ES., Results: Sixty-three subjects underwent extended research EEG. The median duration of the extended research EEG was 24.3 hours (interquartile range 24.0-25.3). Three subjects (5%) had an EEG change during the extended research EEG that resulted in a change in clinical management, including an increase in ES frequency, differential diagnosis of an event, and new interictal epileptiform discharges. No subjects had new-onset ES during the extended research EEG., Conclusions: No subjects experienced new-onset ES during the 24-hour extended research EEG period. This finding supports observational data that patients with late-onset ES are rare and suggests that ES prediction models derived from observational data are likely not substantially underrepresenting the incidence of late-onset ES after discontinuation of clinically indicated CEEG., (Copyright © 2024 by the American Clinical Neurophysiology Society.)

Subclinical rhythmic electrographic discharge of adults (SREDA) is one of the rarest and most challenging non-epileptic electroencephalographic variants. Although the pathogenesis of this activity is unclear, an association with vascular insufficiency and cerebral hypoxia has been proposed. SREDA usually occurs in adulthood, but there are few reports in the pediatric population. We performed a systematic review of the literature, confirming the rarity of this condition in children, and added 5 more subjects. We report on a total of 16 children with SREDA. Sufficient data are available for 15 patients. The mean age at first detection of SREDA was 11.5 years. We observed that 67% (10/15) of the subjects had previous seizures: 80% (8/10) of them had an epilepsy diagnosis and 38% (3/8) had generalized epilepsy. Moreover, 8 of 13 subjects whose medical history was available (61%) had a neurodevelopmental disorder. From an electroencephalographic point of view, we noted a prevalence of bilateral SREDA with atypical localization and abrupt onset and end. Since SREDA can be incorrectly interpreted as an epileptic discharge, with possible therapeutic implications, it is important to consider its possible occurrence also in pediatric patients, perhaps more frequently in those with neurodevelopmental disorders., (© 2024 The Author(s). Epileptic Disorders published by Wiley Periodicals LLC on behalf of International League Against Epilepsy.)

Background: Neonatal seizures are common with acute brain injury. Up to 25% of survivors develop postneonatal epilepsy. We hypothesized postneonatal epilepsy diagnosed by age 24 months would increase risk for early markers of neurobehavioral disorders than acute provoked neonatal seizures alone., Methods: Neonates with acute provoked seizures born from July 2015 to March 2018 were enrolled at nine Neonatal Seizure Registry sites. Composite scores from parent-completed standardized ratings assessed Adaptive, Social, Externalizing, Internalizing, Self-Regulation, and Sensory Seeking domains. Linear regression demonstrated relationships between composite scores for children who developed postneonatal epilepsy compared with those who did not. Results were adjusted for seizure etiology, sex, gestational age, and cerebral palsy (CP) severity., Results: A total of 151 children (n = 20, 13% with postneonatal epilepsy), 4.1 years median age, participated. Children with epilepsy had impaired adaptive (Cohen d = 1.62, P < 0.0001), social (Cohen d = 0.86, P = 0.004), and executive functioning (Cohen d = 0.56, P = 0.06) compared with children without epilepsy. Mean scores for children without epilepsy were within average range. Risk for impairment among children with epilepsy persisted after adjusting for neonatal seizure etiology, sex, and gestational age, but not when adjusting for CP severity., Conclusions: There was higher incidence of adverse neurobehavioral outcomes among preschool children diagnosed with postneonatal epilepsy compared with those without epilepsy. CP severity was associated with greater impairment; results also suggest that epilepsy is an independent predictor of adaptive functioning. Children with postneonatal epilepsy should be screened for neurobehavioral problems to facilitate early identification and developmental support., Competing Interests: Declaration of competing interest Dr. Shellhaas receives royalties from UpToDate for authorship of topics related to neonatal seizures, serves as a consultant for the Epilepsy Study Consortium, and receives a stipend for her role as president-elect of the Pediatric Epilepsy Research Foundation. The other authors declare no competing interests., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Background: Acute encephalopathy with biphasic seizures and late reduced diffusion (AESD) is clinically characterized by biphasic seizures associated with mild to severe neurological sequelae and is the most common subtype of acute encephalopathy in Japan, accounting for around 30 % of cases. The present study retrospectively analyzed the utility of electroencephalography (EEG) in determining the optimal method of diagnosing AESD at the early stage., Methods: This study explores early power value differences to differentiate acute encephalopathy from prolonged febrile seizure (FS). The subjects were patients aged six months to four years who had received intensive care for febrile status epilepticus and had a continuous EEG record up to 24 h after seizure onset. Power spectrum analysis of consecutive, 30-minute recordings at two-hour intervals were conducted, and the power values (alpha, beta, delta, theta waves) were compared between AESD group and FS group., Results: Eight patients with AESD and 69 patients with FS were identified retrospectively. The emergence of delta waves in the frontal region was significantly higher in the AESD group at six to ten hours after onset. The emergence of beta waves across all the regions was lower in the AESD group from six hours after seizure onset., Conclusions: Frequency analysis of EEG in the early period after febrile status epilepticus onset demonstrated a significant difference between the AESD and FS groups. Delta wave power values in the frontal region at six to ten hours after onset might be useful for the early differentiation of AESD from FS., Competing Interests: Declaration of Competing Interest The authors have no conflicts of interest to disclose., (Copyright © 2025 The Authors. Published by Elsevier B.V. All rights reserved.)

Wearable devices with continuous monitoring capabilities are critical for the daily detection of epileptic seizures, as they provide users with accurate and comprehensible analytical results. However, current AI classifiers rely on a two-stage recognition process for continuous monitoring, which only reduces operation time but remains challenged by the high cost of additional hardware. To address this problem, this article proposes a novel fusion architecture for AI processors, which enables event-triggered cross-paradigm integration and computation. Our method introduces a distributed-aggregated classification architecture (D-ACA) that facilitates the reuse of hardware resources across two-stage recognition, thereby obviating the need for standby hardware and enhancing energy efficiency. Integrating a non-encoding biomedical circuit method based on spiking neural networks (SNNs), the architecture eliminates encoded neurons at the hardware level, significantly optimizing energy consumption and hardware resource utilization. Additionally, we develop a configurable and highly flexible control method that supports various neuron modules, enabling continuous detection of epileptic seizures and activating high-precision recognition upon event detection. Finally, we implement the design on the Xilinx ZCU 102 FPGA board, where the AI processor achieves a high classification accuracy of 98.1% while consuming extremely low classification energy (3.73 J per classification).

Objectives: Pediatric status epilepticus (SE) carries a high risk of morbidity and mortality and can result in neurologic injury. Establishing seizure activity on conventional EEG (cEEG) is essential but can delay treatment of seizures due to technician limitations. Rapid response EEG (rrEEG) device Ceribell and its Brain Stethoscope function can be used and interpreted rapidly by bedside providers with minimal training. This retrospective pilot study examines the impact of rrEEG introduction at a quaternary care children's hospital on time to definitive diagnosis and treatment, as well as the accuracy of the Brain Stethoscope., Methods: This was a single center retrospective observational cohort study that analyzed data from patients 2-18 years old who presented with concerns for SE. For rrEEG patients, the bedside physician used the Brain Stethoscope at four discrete points. TDEA (time to diagnosis of electrographic activity) and setup time were recorded and compared using Welch's T-test. Diagnostic specificity and sensitivity for SE using the Brain Stethoscope were calculated against the epileptologist's assessment., Results: Data were collected from 30 pediatric patients, 15 on each EEG modality. RrEEG decreased the average TDEA (132 min vs. 22 min, p < 0.001) and setup time (22 min vs. 9 min, p < 0.001), compared to the cEEG. Bedside physicians diagnosed electrographic activity using the Brain Stethoscope with 100% sensitivity (95% CI 63%-100%) and 92% specificity (95% CI 81%-97%). RrEEG ruled out seizures in 11 patients and changed clinical decision-making in five patients., Significance: RrEEG allowed for earlier diagnosis of brain electrographic activity in pediatric patients when compared to cEEG. The bedside provider was able to initiate EEG monitoring, successfully diagnose patients using the Brain Stethoscope, and decrease delays associated with technician availability. This promising rrEEG technology can facilitate faster assessment of SE in pediatric acute care settings, potentially reducing ongoing neurologic injury., Plain Language Summary: Prolonged seizures in pediatric patients can cause death. Children can have seizures that are happening in the brain, but cannot be seen physically. They can be diagnosed by a machine that records the brain's electrical activity and the data can be interpreted by a specialized neurologist, but the process of getting the machine connected to the patient and getting the data often takes hours and delays diagnosis. This study evaluated a new machine called the Ceribell® and determined that it facilitated a much faster diagnosis than the conventional machine in children and helped the bedside physician interpret the data quickly., (© 2025 The Author(s). Epilepsia Open published by Wiley Periodicals LLC on behalf of International League Against Epilepsy.)

Objective: This article provides current evidence on how and when to treat unprovoked first seizures in children and adults, guides intervention with appropriate doses and types of modern and effective therapies for acute repetitive (cluster) seizures, and reviews evidence for the diagnosis and management of established, refractory and super-refractory status epilepticus., Latest Developments: Artificial intelligence shows promise as a clinical assistant in decision making after a first seizure. For nonanoxic convulsive refractory status epilepticus third-phase treatment, equipoise exists regarding whether it is better to add a second IV nonsedating antiseizure medication given via loading dose (eg, brivaracetam, lacosamide, levetiracetam, fosphenytoin or valproic acid) or to start an anesthetizing continuous IV infusion antiseizure medication such as ketamine, midazolam, propofol or pentobarbital., Essential Points: After a first seizure, the risk of a second seizure is about 36% at 2 years and 46% after 5 years. The risk is doubled in the presence of EEG epileptiform discharges, a brain imaging abnormality, a nocturnal first seizure, or prior brain trauma. For acute repetitive seizures, providers should give a proper dose of benzodiazepines based on the patient's weight and needs. First-phase treatment for convulsive established status epilepticus is the immediate administration of full doses of benzodiazepines. Second-phase treatment for convulsive established status epilepticus is a full loading dose of IV fosphenytoin, levetiracetam, valproic acid, or if necessary, phenobarbital., (Copyright © 2025 American Academy of Neurology.)

Purpose: This study reports our center's initial experience with the use of low-frequency stimulation in provoking stimulation-induced seizures (SIS) in children with drug-resistant epilepsy undergoing stereo-EEG evaluations., Methods: This retrospective study enrolled children aged 2 to 18 years with drug-resistant focal epilepsy who underwent stereo-EEG evaluation and extraoperative direct electrical cortical stimulation to elicit seizures. The low-frequency stimulation parameters consisted of biphasic square waveforms at frequency of 1 Hz, pulse width 1 millisecond, current 1 to 3 mA, and train duration of 20 seconds. Various epilepsy-related, imaging, neurophysiology, and surgery-related variables were collected and summarized., Results: Fourteen children (mean age 13 years; 57.1% girls) were included, 10 of whom had unilateral stereo-EEG coverage. Cortical stimulation for provoking seizures was performed after a median of 5 days after electrode implantation. The median number of electrode-contacts stimulated per patient was 42. Four patients (28.6%) experienced habitual SIS (all extratemporal). The etiology in three patients was focal cortical dysplasia. Interictal high-frequency oscillations at electrode-contacts provoking SIS were observed in three cases (75%). Two of these individuals (50%) had class 1 International League Against Epilepsy seizure outcome at last follow-up, after the resection of the brain regions generating SIS., Conclusions: Low-frequency (1-Hz) stimulation could provoke habitual SIS in nearly one-fourth of children with focal epilepsy undergoing stereo-EEG monitoring. This study provides a limited pediatric experience with the low-frequency cortical stimulation and SIS., Competing Interests: The authors have no funding or conflicts of interest to disclose., (Copyright © 2024 by the American Clinical Neurophysiology Society.)

Objective: While guidelines encourage individualized discussions of the risks and benefits of antiseizure medication (ASM) withdrawal after a period of seizure-freedom, no formal methods exist for assessing patient preferences. We report the initial development of a rapid patient preferences screener., Methods: We conducted a mixed-methods study of adults who were ≥1 year seizure-free and seen for epilepsy across three institutions. We reviewed existing questionnaires and adapted three questions measuring core constructs influencing ASM decisions - views about ASMs, driving restrictions, and seizures. We added one additional "global" question, for 4 total questions. Participants rated question clarity and utility from 1 (low) to 7 (high)., Results: Of 32 participants, the median patient age was 46 (interquartile range [IQR] 33-56), with a median 3 years since their last seizure (IQR 2-11). Median responses were: 2 (IQR 1-5) for being bothered by ASMs, 2 (IQR 1-6) for feeling that a driving restriction would be disruptive, and 5 (IQR 4-7) for feeling that another seizure would be serious. Respondents tended to disagree that ASMs are doing more harm than good (median 1, IQR 1-2). Participants rated question clarity (median 6, IQR 6-7) and utility (median 7, IQR 6-7) highly., Conclusions: We report the initial development of a pre-visit rapid screener of patient preferences pertinent to ASM withdrawal in well-controlled epilepsy. Patients endorsed the utility of such a screener and provided guidance to improve items. We hope that this work will ultimately improve shared decision-making., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: The authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper. Dr Terman was recently supported by an American Epilepsy Society Research and Training Fellowship for Clinicians. He is now supported by the National Institutes of Health K23 AG081463. He has been a paid consultant for Jazz Pharmaceuticals and is currently a paid consultant for Xenon Pharmaceuticals. Dr Hill is funded by NIH K23NS126495. Other authors report no relevant funding., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Objective: Neonatal encephalopathy (NE) is a leading cause of childhood death and disability, particularly in sub-Saharan Africa. Detection of NE-related seizures is challenging. We explored NE seizure semiology and management in Uganda., Methods: Video-EEG was recorded (days 1-5), seizure semiology reviewed according to ILAE classification and administration of antiseizure medication (ASM) evaluated. Clinicians treated seizures based on the clinical presentation alone., Results: Among 50 participants, 52% (26) had EEG-confirmed seizures; 70% (18) combined electroclinical/electrographic; 4% (1) exclusively electroclinical; 22% (6) electrographic. Of those with electroclinical seizures (19), 42% displayed >1 semiology. Distribution of seizure semiology was; clonic 34% (11); autonomic 24% (8, of which 6 had prolonged ictal apnea); automatisms 18% (6); behavioral arrest 12% (4); and sequential 12% (4). ASM was administered to 64% (32/50). Of those with EEG-confirmed seizures, only 62% (16/26) received ASM. In the non-seizure group, 38% (9/24) received ASM during monitoring. ASM was administered 42 times, of which 45% (19) were considered appropriate., Significance: In this Ugandan NE population, incidence of seizures was high and clinical manifestations frequent. Clonic, autonomic and automatisms were most common. Clinical management was challenging, with both under and overtreatment evident. Respiratory impairment due to autonomic seizures frequently went unrecognized and is a prominent concern, particularly in settings without neonatal intensive care., (© 2024 The Author(s). Epileptic Disorders published by Wiley Periodicals LLC on behalf of International League Against Epilepsy.)

Objective: Electroencephalographic seizures (ES) are common in neonates with hypoxic-ischemic encephalopathy (HIE), but identification with continuous electroencephalographic (EEG) monitoring (CEEG) is resource-intensive. We aimed to develop an ES prediction model., Methods: Using a prospective observational study of 260 neonates with HIE undergoing CEEG, we identified clinical and EEG risk factors for ES, evaluated model performance with area under the receiver operating characteristic curve (AUROC), and calculated test characteristics emphasizing high sensitivity. We assessed ES incidence and timing in neonates subdivided by ES risk group (low, moderate, high) as determined by EEG risk factors., Results: ES occurred in 32% (83/260) of neonates. Performing CEEG for only 24 h would fail to identify the 7% (17/260) of neonates with later onset ES (20% of all neonates experiencing ES). Identifying 90% or 95% of neonates with ES would require CEEG for 63 or 74 h, respectively. The optimal model included continuity and epileptiform discharges, both assessed in the initial 1 h of CEEG. It yielded an AUROC of .80, and at a cutoff that emphasized sensitivity, had sensitivity of 94%, specificity of 45%, positive predictive value of 44%, and negative predictive value of 95%. The model would avoid CEEG beyond 1 h in 32% (84/260) of neonates, but 6% (5/83) of neonates with ES would not have ES identified. ES incidence was significantly different (p < .01) across ES risk groups (6% low, 40% moderate, and 83% high). Only ~6 h of CEEG would identify all neonates with ES in the low-risk group, whereas 75 and 63 h of CEEG would be required to identify 95% of neonates with ES in the moderate-risk and high-risk groups, respectively., Significance: Among neonates with HIE, a model employing two EEG variables from a 1-h screening EEG and stratifying neonates into low-, moderate-, and high-risk groups could enable evidence-based strategies for targeted CEEG use., (© 2024 The Author(s). Epilepsia published by Wiley Periodicals LLC on behalf of International League Against Epilepsy.)

Epilepsy is a neurological disorder characterized by recurrent epileptic seizures, which are often unpredictable and increase mortality and morbidity risks., Objective: The objective of this study is to address the challenges of EEG-based epileptic seizure detection by introducing a novel methodology, Deep Embedded Gaussian Mixture (DEGM)., Methods: The DEGM method begins with a deep autoencoder (DAE) for embedding the input EEG data, followed by Singular Value Decomposition (SVD) to enhance the representational quality of the embedding while achieving dimensionality reduction. A Gaussian Mixture Model (GMM) is then employed for clustering purposes. Unlike conventional supervised machine learning and deep learning techniques, DEGM leverages deep clustering (DC) algorithms for more effective seizure detection., Results: Empirical results from two real-world epileptic datasets demonstrate the notable performance of DEGM. The method's effectiveness is particularly remarkable given the substantial size of the datasets, showcasing its ability to handle large-scale EEG data efficiently., Conclusion: In conclusion, the DEGM methodology provides a novel and effective approach for EEG-based epileptic seizure detection, addressing key challenges such as data variability and artifact contamination., Significance: By combining deep autoencoders, SVD, and GMM, DEGM achieves superior clustering performance compared to existing methods, representing a significant advancement in biomedical research and clinical applications for epilepsy. Its robust performance on large datasets underscores its potential for improving seizure detection accuracy, ultimately contributing to better patient outcomes.

Background and Objectives: The use of rapid response EEG (rr-EEG) has recently expanded in limited-resource settings and as a supplement to conventional EEG to rapidly detect and treat nonconvulsive status epilepticus. The study objective was to test the accuracy of an rr-EEG's automated seizure burden estimator (ASBE)., Methods: This is a retrospective observational study using multiple blinded reviewers. All consecutive clinical rr-EEG procedures performed between November 2019 and February 2021 at Yale New Haven Hospital, one affiliated community hospital, and one affiliated inner-city regional hospital were included. Three reviewers blindly reviewed each EEG. The reference standard was 2/3 agreement. The co-primary outcome measures were the negative predictive value (NPV) of the ASBE for the detection of electrographic status epilepticus (ESE) or possible ESE (ESE/pESE) (to be used as a screening method to exclude ESE without the need for urgent expert review) and the positive predictive value (PPV, to be used for immediate treatment without requiring urgent expert review). These were assessed using a variety of seizure burden cutoffs determined by the algorithm (>1%, >10%, >20%, >50%, and >90%)., Results: In the first 2 hours, a >10% burden cutoff detected 86% (95% CI 42%-100%) of studies with ESE alone and 88% (68%-97%) with ESE/pESE; this >10% cutoff had a NPV of 99% (97%-100%) for ESE and 98% (95%-100%) for ESE/pESE. The specificity at this threshold was 79% (73%-84%) for ESE and 84% (79%-89%) for ESE/pESE, but the PPV was low at 11% (4%-23%) for ESE and 39% (26%-53%) for ESE/pESE. A >90% burden cutoff was 97% (94%-99%) specific for detecting ESE (PPV 33% [7%-70%]) and 99% (97%-100%) specific for detecting ESE/pESE [PPV 78% (40%-97%)], although the sensitivity dropped significantly to 29% (13%-51%) for ESE/pESE and 43% (10%-82%) for ESE at the >90% threshold., Discussion: The ASBE has high specificity at >90% seizure burden threshold for detecting ESE and ESE/pESE, with good PPV for ESE/pESE, though with only low-to-moderate sensitivity; at this threshold, it can be used to help triage patients for immediate treatment/transfer, urgent expert review, and additional CEEG. A >10% threshold has a high sensitivity, detecting approximately 85% of patients with ESE; at this lower cutoff, it can be used as a screening tool to exclude ESE with >95% NPV., Classification of Evidence: This study provides Class II evidence that ASBE software can reliably exclude ESE (98% negative predictive value using a <10% burden cutoff) without expert review in most patients requiring rapid response EEG.

Epilepsy is a prevalent neurological disorder that affects approximately 1% of the global population. Approximately 30-40% of patients respond poorly to antiepileptic medications, leading to a significant negative impact on their quality of life. Closed-loop deep brain stimulation (DBS) is a promising treatment for individuals who do not respond to medical therapy. To achieve effective seizure control, algorithms play an important role in identifying relevant electrographic biomarkers from local field potentials (LFPs) to determine the optimal stimulation timing. In this regard, the detection and classification of events from ongoing brain activity, while achieving low power consumption through computationally inexpensive implementations, represents a major challenge in the field. To address this challenge, we here present two algorithms, the ZdensityRODE and the AMPDE, for identifying relevant events from LFPs by utilizing time series segmentation (TSS), which involves extracting different levels of information from the LFP and relevant events from it. The algorithms were validated validated against epileptiform activity induced by 4-aminopyridine in mouse hippocampus-cortex (CTX) slices and recorded via microelectrode array, as a case study. The ZdensityRODE algorithm showcased a precision and recall of 93% for ictal event detection and 42% precision for interictal event detection, while the AMPDE algorithm attained a precision of 96% and recall of 90% for ictal event detection and 54% precision for interictal event detection. While initially trained specifically for detecting ictal activity, these algorithms can be fine-tuned for improved interictal detection, aiming at seizure prediction. Our results suggest that these algorithms can effectively capture epileptiform activity, supporting seizure detection and, possibly, seizure prediction and control. This opens the opportunity to design new algorithms based on this approach for closed-loop stimulation devices using more elaborate decisions and more accurate clinical guidelines., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2025 Galeote-Checa et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)