Prognostic Value of Complete Resection of the High-Frequency Oscillation Area in Intracranial EEG: A Systematic Review and Meta-Analysis.

BACKGROUND AND OBJECTIVES: High-frequency oscillations (HFOs; ripples 80-250 Hz; fast ripples [FRs] 250-500 Hz) recorded with intracranial electrodes generated excitement and debate about their potential to localize epileptogenic foci. We performed a systematic review and meta-analysis on the prognostic value of complete resection of the HFOs-area (crHFOs-area) for epilepsy surgical outcome in intracranial EEG (iEEG) accessing multiple subgroups. METHODS: We searched PubMed, Embase, and Web of Science for original research from inception to October 27, 2022. We defined favorable surgical outcome (FSO) as Engel class I, International League Against Epilepsy class 1, or seizure-free status. The prognostic value of crHFOs-area for FSO was assessed by (1) the pooled FSO proportion after crHFOs-area; (2) FSO for crHFOs-area vs without crHFOs-area; and (3) the predictive performance. We defined high combined prognostic value as FSO proportion >80% + FSO crHFOs-area >without crHFOs-area + area under the curve (AUC) >0.75 and examined this for the clinical subgroups (study design, age, diagnostic type, HFOs-identification method, HFOs-rate thresholding, and iEEG state). Temporal lobe epilepsy (TLE) was compared with extra-TLE through dichotomous variable analysis. Individual patient analysis was performed for sex, affected hemisphere, MRI findings, surgery location, and pathology. RESULTS: Of 1,387 studies screened, 31 studies (703 patients) met our eligibility criteria. Twenty-seven studies (602 patients) analyzed FRs and 20 studies (424 patients) ripples. Pooled FSO proportion after crHFOs-area was 81% (95% CI 76%-86%) for FRs and 82% (73%-89%) for ripples. Patients with crHFOs-area achieved more often FSO than those without crHFOs-area (FRs odds ratio [OR] 6.38, 4.03-10.09, p < 0.001; ripples 4.04, 2.32-7.04, p < 0.001). The pooled AUCs were 0.81 (0.77-0.84) for FRs and 0.76 (0.72-0.79) for ripples. Combined prognostic value was high in 10 subgroups: retrospective, children, long-term iEEG, threshold (FRs and ripples) and automated detection and interictal (FRs). FSO after complete resection of FRs-area (crFRs-area) was achieved less often in people with TLE than extra-TLE (OR 0.37, 0.15-0.89, p = 0.006). Individual patient analyses showed that crFRs-area was seen more in patients with FSO with than without MRI lesions (p = 0.02 after multiple correction). DISCUSSION: Complete resection of the brain area with HFOs is associated with good postsurgical outcome. Its prognostic value holds, especially for FRs, for various subgroups. The use of HFOs for extra-TLE patients requires further evidence.

Clinically significant changes in genes and variants associated with epilepsy over time: implications for re-analysis.

Despite the significant advances in understanding the genetic architecture of epilepsy, many patients do not receive a molecular diagnosis after genomic testing. Re-analysing existing genomic data has emerged as a potent method to increase diagnostic yields-providing the benefits of genomic-enabled medicine to more individuals afflicted with a range of different conditions. The primary drivers for these new diagnoses are the discovery of novel gene-disease and variants-disease relationships; however, most decisions to trigger re-analysis are based on the passage of time rather than the accumulation of new knowledge. To explore how our understanding of a specific condition changes and how this impacts re-analysis of genomic data from epilepsy patients, we developed Vigelint. This approach combines the information from PanelApp and ClinVar to characterise how the clinically relevant genes and causative variants available to laboratories change over time, and this approach to five clinical-grade epilepsy panels. Applying the Vigelint pipeline to these panels revealed highly variable patterns in new, clinically relevant knowledge becoming publicly available. This variability indicates that a more dynamic approach to re-analysis may benefit the diagnosis and treatment of epilepsy patients. Moreover, this work suggests that Vigelint can provide empirical data to guide more nuanced, condition-specific approaches to re-analysis.

Concept-drifts adaptation for machine learning EEG epilepsy seizure prediction.

Seizure prediction remains a challenge, with approximately 30% of patients unresponsive to conventional treatments. Addressing this issue is crucial for improving patients' quality of life, as timely intervention can mitigate the impact of seizures. In this research field, it is critical to identify the preictal interval, the transition from regular brain activity to a seizure. While previous studies have explored various Electroencephalogram (EEG) based methodologies for prediction, few have been clinically applicable. Recent studies have underlined the dynamic nature of EEG data, characterised by data changes with time, known as concept drifts, highlighting the need for automated methods to detect and adapt to these changes. In this study, we investigate the effectiveness of automatic concept drift adaptation methods in seizure prediction. Three patient-specific seizure prediction approaches with a 10-minute prediction horizon are compared: a seizure prediction algorithm incorporating a window adjustment method by optimising performance with Support Vector Machines (Backwards-Landmark Window), a seizure prediction algorithm incorporating a data-batch (seizures) selection method using a logistic regression (Seizure-batch Regression), and a seizure prediction algorithm with a dynamic integration of classifiers (Dynamic Weighted Ensemble). These methods incorporate a retraining process after each seizure and use a combination of univariate linear features and SVM classifiers. The Firing Power was used as a post-processing technique to generate alarms before seizures. These methodologies were compared with a control approach based on the typical machine learning pipeline, considering a group of 37 patients with Temporal Lobe Epilepsy from the EPILEPSIAE database. The best-performing approach (Backwards-Landmark Window) achieved results of 0.75 ± 0.33 for sensitivity and 1.03 ± 1.00 for false positive rate per hour. This new strategy performed above chance for 89% of patients with the surrogate predictor, whereas the control approach only validated 46%.

Automatic Detection of Scalp High-Frequency Oscillations Based on Deep Learning.

Scalp high-frequency oscillations (sHFOs) are a promising non-invasive biomarker of epilepsy. However, the visual marking of sHFOs is a time-consuming and subjective process, existing automatic detectors based on single-dimensional analysis have difficulty with accurately eliminating artifacts and thus do not provide sufficient reliability to meet clinical needs. Therefore, we propose a high-performance sHFOs detector based on a deep learning algorithm. An initial detection module was designed to extract candidate high-frequency oscillations. Then, one-dimensional (1D) and two-dimensional (2D) deep learning models were designed, respectively. Finally, the weighted voting method is used to combine the outputs of the two model. In experiments, the precision, recall, specificity and F1-score were 83.44%, 83.60%, 96.61% and 83.42%, respectively, on average and the kappa coefficient was 80.02%. In addition, the proposed detector showed a stable performance on multi-centre datasets. Our sHFOs detector demonstrated high robustness and generalisation ability, which indicates its potential applicability as a clinical assistance tool. The proposed sHFOs detector achieves an accurate and robust method via deep learning algorithm.

Trastornos del sueño en niños con epilepsia / Sleep disorders in children with epilepsy

Introducción: Los niños con epilepsia tienen más trastornos del sueño (TS) que la población sana. Es fundamental su diagnóstico, ya que la epilepsia y los TS tienen una relación bidireccional. Objetivo: Determinar la incidencia de TS y malos hábitos de sueño en niños con epilepsia. Método: Estudio transversal de pacientes menores de 18 años con epilepsia sobre TS, mediante la versión española de Sleep Disturbance Scale for Children (SDSC), y sobre hábitos de sueño, mediante cuestionario de elaboración propia. Resultados: La muestra incluyó 153 pacientes. El 84% de la población estudiada presentaba alterado algún aspecto del sueño. Lo más frecuente fueron las alteraciones en la transición sueño-vigilia (53%), en el inicio-mantenimiento del sueño (47,7%) y la somnolencia diurna (44,4%). Un 70% de los padres de los pacientes referían que su hijo «dormía bien», pero en este grupo se detectaron TS hasta en el 75,7%. Muchos de los pacientes tenían hábitos de sueño poco saludables, como dormirse con dispositivos electrónicos (16,3%), precisar presencia familiar para dormirse (39%) o dormir en colecho o cohabitación (23,5 y 30,5%, respectivamente). Aquellos con epilepsias generalizadas, refractarias, crisis nocturnas y discapacidad intelectual presentaron mayor probabilidad de presentar TS. En cambio, los malos hábitos de sueño fueron frecuentes independientemente de las características de la epilepsia. Conclusiones: Los TS y los malos hábitos de sueño son frecuentes en niños con epilepsia. Su tratamiento puede conllevar una mejoría en la calidad de vida del paciente y su familia, así como una mejoría en el pronóstico de la epilepsia.(AU)

Introduction: Children with epilepsy present greater prevalence of sleep disorders than the general population. Their diagnosis is essential, since epilepsy and sleep disorders have a bidirectional relationship. Objective: Determine the incidence of sleep disorders and poor sleep habits in children with epilepsy. Methods: We conducted a cross-sectional study of patients under 18 years of age with epilepsy, assessing sleep disorders using the Spanish-language version of the Sleep Disturbance Scale for Children (SDSC), and sleep habits using an original questionnaire. Results: The sample included 153 patients. Eighty-four percent of our sample presented some type of sleep alteration. The most frequent alterations were sleep-wake transition disorders (53%), sleep initiation and maintenance disorders (47.7%), and daytime sleepiness (44.4%). In 70% of cases, the patients’ parents reported that their child “slept well,” although sleep disorders were detected in up to 75.7% of these patients. Many patients had poor sleep habits, such as using electronic devices in bed (16.3%), requiring the presence of a family member to fall asleep (39%), or co-sleeping or sharing a room (23.5% and 30.5%, respectively). Those with generalised epilepsy, refractory epilepsy, nocturnal seizures, and intellectual disability were more likely to present sleep disorders. In contrast, poor sleep habits were frequent regardless of seizure characteristics. Conclusions: Sleep disorders and poor sleep habits are common in children with epilepsy. Their treatment can lead to an improvement in the quality of life of the patient and his/her family, as well as an improvement in the prognosis of epilepsy.(AU)

Autoimmune and paraneoplastic seizures.

Seizures are a common feature of autoimmune encephalitis and are especially prevalent in patients with the commonest autoantibodies, against LGI1, CASPR2 and the NMDA, GABAB, and GABAA receptors. In this chapter, we discuss the classification, clinical, investigation, and treatment aspects of patients with these, and other autoantibody-mediated and -associated, illnesses. We highlight distinctive and common seizure semiologies which, often alongside other features we outline, can help the clinical diagnosis of an autoantibody-associated syndrome. Next, we classify these syndromes by either focusing on whether they represent underlying causative autoantibodies or T-cell-mediated syndromes and on the distinction between acute symptomatic seizures and a more enduring tendency to autoimmune-associated epilepsy, a practical and valuable distinction for both patients and clinicians which relates to the pathogenesis. We emphasize the more effective immunotherapy response in patients with causative autoantibodies, and discuss the emerging evidence for various first-, second-, and third-line immunotherapies. Finally, we highlight available clinical rating scales which can guide autoantibody testing and immunotherapy in patients with seizures of unknown etiology. Throughout, we relate the clinical and therapeutic observations to the immunobiology and neuroscience which drive these seizures.

[Clinical and genetic spectrum of 6 cases with asparagine synthetase deficiency].

Objective: To explore the clinical and genetic characteristics of asparagine synthase deficiency. Methods: Case series studies. Retrospective analysis and summary of the clinical data of 6 cases with asparagine synthase deficiency who were diagnosed by genetic testing and admitted to the Third Affiliated Hospital of Zhengzhou University from May 2017 to April 2023 were analyzed retrospectively. The main clinical features, laboratory and imaging examination characteristics of the 6 cases were summarized, and the gene variation sites of them were analyzed. Results: All of the 6 cases were male, with onset ages ranging from 1 month to 1 year and 4 months. All of the 6 cases had cognitive and motor developmental delay, with 3 cases starting with developmental delay, 3 cases starting with convulsions and later experiencing developmental arrest or even regression. All of 6 cases had epilepsy, in whom 2 cases with severe microcephaly developed epileptic encephalopathy in the early stages of infancy with spasms as the main form of convulsions, 4 cases with mild or no microcephaly gradually evolved into convulsions with no fever after multiple febrile convulsions with focal seizures, tonic clonic seizures and tonic seizure as the main forms of convulsions. Three cases of 4 gradually developed into stagnation or even regression of development and ataxia after multiple convulsions with no fever. There were normal cranial imaging in 2 cases, dysplasia of the brains in 1 cases, frontal lobe apex accompanied by abnormal white matter signal in the frontal lobe and thin corpus callosum in 1 case, thin corpus callosum and abnormal lateral ventricular morphology in 1 case, and normal in early stage, but gradually developing into cerebellar atrophy at the age of 5 years and 9 months in 1 case. Two cases underwent visual evoked potential tests, the results of which were both abnormal. Three cases underwent auditory evoked potential examination, with 1 being normal and 2 being abnormal. All of 6 cases had variations in the asparagine synthase gene, with 2 deletion variations and 7 missense variations. The variations of 2 cases had not been reported so far, including c.1341_1343del and c.1283A>G, c.1165_1167del and c.1075G>A. The follow-up time ranged from 3 months to 53 months. Two cases who had severe microcephaly died in infancy, while the other 4 cases with mild or no microcephaly were in survival states until the follow-up days but the control of epilepsy was poor. Conclusions: Asparagine synthase deficiency has a certain degree of heterogeneity in clinical phenotype. Children with obvious microcephaly often present as severe cases, while children with mild or no microcephaly have relatively mild clinical manifestations. The variation of asparagine synthetase gene is mainly missense variation.

Ultra-Long-Term-EEG Monitoring (ULTEEM) Systems: Towards User-Friendly Out-of-Hospital Recordings of Electrical Brain Signals in Epilepsy.

Epilepsy is characterized by the occurrence of epileptic events, ranging from brief bursts of interictal epileptiform brain activity to their most dramatic manifestation as clinically overt bilateral tonic-clonic seizures. Epileptic events are often modulated in a patient-specific way, for example by sleep. But they also reveal temporal patterns not only on ultra- and circadian, but also on multidien scales. Thus, to accurately track the dynamics of epilepsy and to thereby enable and improve personalized diagnostics and therapies, user-friendly systems for long-term out-of-hospital recordings of electrical brain signals are needed. Here, we present two wearable devices, namely ULTEEM and ULTEEMNite, to address this unmet need. We demonstrate how the usability concerns of the patients and the signal quality requirements of the clinicians have been incorporated in the design. Upon testbench verification of the devices, ULTEEM was successfully benchmarked against a reference EEG device in a pilot clinical study. ULTEEMNite was shown to record typical macro- and micro-sleep EEG characteristics in a proof-of-concept study. We conclude by discussing how these devices can be further improved and become particularly useful for a better understanding of the relationships between sleep, epilepsy, and neurodegeneration.

De novo variants of IRF2BPL result in developmental epileptic disorder.

BACKGROUND: Pathogenic variants of the IRF2BPL gene have been reported to cause neurodevelopmental disorders; however, studies focused on IRF2BPL in zebrafish are limited. RESULTS: We reported three probands diagnosed with developmental delay and epilepsy and investigated the role of IRF2BPL in neurodevelopmental disorders in zebrafish. The clinical and genetic characteristics of three patients with neurodevelopmental disorder with regression, abnormal movements, loss of speech and seizures (NEDAMSS) were collected. Three de novo variants (NM_024496.4: c.1171 C > T, p.Arg391Cys; c.1157 C > T, p.Thr386Met; and c.273_307del, p.Ala92Thrfs*29) were detected and classified as pathogenic or likely pathogenic according to ACMG guidelines. Zebrafish crispants with disruption of the ortholog gene irf2bpl demonstrated a reduced body length and spontaneous ictal-like and interictal-like discharges in an electrophysiology study. After their spasms were controlled, they gain some development improvements. CONCLUSION: We contribute two new pathogenic variants for IRF2BPL related developmental epileptic disorder which provided evidences for genetic counseling. In zebrafish model, we for the first time confirm that disruption of irf2bpl could introduce spontaneous electrographic seizures which mimics key phenotypes in human patients. Our follow-up results suggest that timely cessation of spasmodic seizures can improve the patient's neurodevelopment.

Etiological analysis of 167 cases of drug-resistant epilepsy in children.

BACKGROUND: To analyze the etiological distribution characteristics of drug-resistant epilepsy (DRE) in children, with the aim of providing valuable perspectives to enhance clinical practice. METHODS: In this retrospective study, clinical data were collected on 167 children with DRE who were hospitalized between January 2020 and December 2022, including gender, age of onset, seizure types, video electroencephalogram(VEEG) recordings, neuroimaging, and genetic testing results. Based on the etiology of epilepsy, the enrolled children were categorized into different groups. The rank-sum test was conducted to compare the age of onset for different etiologies. RESULTS: Of the 167 cases, 89 (53.3%) had a clear etiology. Among them, structural factors account for 23.4%, genetic factors for 19.2%, multiple factors for 7.2%, and immunological factors for 3.6%. The age of onset was significantly earlier in children with genetic causes than those with structural (P < 0.001) or immunological (P = 0.001) causes. CONCLUSIONS: More than half of children with DRE have a distinct underlying cause, predominantly attributed to structural factors, followed by genetic factors. Genetic etiology primarily manifests at an early age, especially among children aged less than one year. This underscores the need for proactive enhancements in genetic testing to unveil the underlying causes and subsequently guide treatment protocols.

Effects of a Nurse-Led Cognitive Behavioral Intervention for Parents of Children With Epilepsy.

BACKGROUND: This study aimed to evaluate the effects of a nurse-led cognitive behavioral intervention for parents of children with epilepsy (CWE). METHODS: The study recruited 238 CWE from the neurology ward of Xiangya Hospital from March 2019 to August 2022. According to the interventions after discharge, the children and their parents were randomly divided into 117 parent-child dyads in the intervention group and 121 parent-child dyads in the control group. The seizure severity and treatment compliance in CWE as well as the parents' psychological states and satisfaction with the care provided by nurses were compared before and after intervention. RESULTS: The follow-up six months after discharge showed that the seizure frequency among CWE in the intervention group was significantly less than the controls (P = 0.048). Compared with the controls, the intervention group also reported fewer symptoms of anxiety and depression, better sleep quality, and more positive attitudes toward epilepsy, as well as higher nursing satisfaction (P < 0.001). The correlation analysis indicated the correlation of CWE's seizure severity was correlated with the compliance, parents' psychological states, and parents' satisfaction with the care provided by nurses. CONCLUSIONS: The adoption of the nurse-led cognitive behavioral intervention on parents of CWE can improve the parents' mental health status and their satisfaction with the nurses, which can have a positive association with the seizure severity of CWE. In light of this information, this nursing intervention may be a new method for the long-term disease management of CWE.

Cosine convolutional neural network and its application for seizure detection.

Traditional convolutional neural networks (CNNs) often suffer from high memory consumption and redundancy in their kernel representations, leading to overfitting problems and limiting their application in real-time, low-power scenarios such as seizure detection systems. In this work, a novel cosine convolutional neural network (CosCNN), which replaces traditional kernels with the robust cosine kernel modulated by only two learnable factors, is presented, and its effectiveness is validated on the tasks of seizure detection. Meanwhile, based on the cosine lookup table and KL-divergence, an effective post-training quantization algorithm is proposed for CosCNN hardware implementation. With quantization, CosCNN can achieve a nearly 75% reduction in the memory cost with almost no accuracy loss. Moreover, we design a configurable cosine convolution accelerator on Field Programmable Gate Array (FPGA) and deploy the quantized CosCNN on Zedboard, proving the proposed seizure detection system can operate in real-time and low-power scenarios. Extensive experiments and comparisons were conducted using two publicly available epileptic EEG databases, the Bonn database and the CHB-MIT database. The results highlight the performance superiority of the CosCNN over traditional CNNs as well as other seizure detection methods.

Emergence of lingual dystonia and strabismus in early-onset SCN8A self-limiting familial infantile epilepsy.

Pathogenic variants in SCN8A are associated with a broad phenotypic spectrum, including Self-Limiting Familial Infantile Epilepsy (SeLFIE), characterized by infancy-onset age-related seizures with normal development and cognition. Movement disorders, particularly paroxysmal kinesigenic dyskinesia typically arising after puberty, may represent another core symptom. We present the case of a 1-year-old girl with a familial disposition to self-limiting focal seizures from the maternal side and early-onset orofacial movement disorders associated with SCN8A-SeLFIE. Brain MRI was normal. Genetic testing revealed a maternally inherited SCN8A variant [c.4447G > A; p.(Glu1483Lys)]. After the introduction of valproic acid, she promptly achieved seizure control as well as complete remission of strabismus and a significant decrease in episodes of tongue deviation. Family history, genetic findings, and epilepsy phenotype are consistent with SCN8A-SeLFIE. Movement disorders are an important part of the SCN8A phenotypic spectrum, and this case highlights the novel early-onset orofacial movement disorders associated with this condition. The episodes of tongue deviation and protrusion suggest focal oromandibular (lingual) dystonia. Additionally, while infantile strabismus or esophoria is a common finding in healthy individuals, our case raises the possibility of an ictal origin of the strabismus. This study underscores the importance of recognizing and addressing movement disorders in SCN8A-SeLFIE patients, particularly the rare early-onset orofacial manifestations. It adds to the growing body of knowledge regarding the diverse clinical presentations of SCN8A-associated disorders and suggests potential avenues for clinical management and further research.

[Epilepsy and pregnancy]. / Epilepsie und Schwangerschaft.

BACKGROUND: Regarding treatment of women of childbearing potential with epilepsy, several aspects of family planning and desire to have children have to be taken into account. OBJECTIVE: Overview of current data on mutual implications of epileptic seizures, antiseizure medication (ASM), pregnancy and child development. METHOD: Review of the current literature, discussion and presentation of resulting treatment recommendations. RESULTS: Many ASMs bear the potential for clinically relevant interactions with both contraceptives and altered concentrations of sexual hormones and modified pharmacokinetics during pregnancy. All ASMs show an increased risk for congenital malformations; however, due to seizure-related risks for the mother and child effective ASM treatment during pregnancy is crucial. CONCLUSION: When considering the special aspects of consultation and treatment of women of childbearing potential with epilepsy most pregnancies are uncomplicated.

The impact of simultaneous intracranial recordings on scalp EEG: A finite element analysis.

BACKGROUND: In this study, we examined the utility of simultaneous scalp and stereotactic intracranial electroencephalography (SSIEEG) in epilepsy patients. Although SSIEEG offers valuable insights into epilepsy and cognitive function, its routine use is uncommon. Challenges include interpreting post-craniotomy scalp EEG due to surgically implanted electrodes. NEW METHOD: We describe our methodology for conducting SSIEEG recordings. To simulate the potential impact on EEG interpretation, we computed the leadfield of scalp electrodes with and without burrholes using Finite Element Analysis to compare the resulting sensitivity volume and waveforms of simulated intracranial signals between skulls with and without burrholes. RESULTS: The presence of burr holes in the skull layer of the leadfield models did not discernibly modify simulated waveforms or scalp EEG topology. Using realistic SEEG burr hole diameter, the difference in the average leadfield of scalp electrodes was 0.12% relative to the effect of switching two nearby electrodes, characterized by the cosine similarity difference. No patients experienced adverse events related to SSIEEG. COMPARISON WITH EXISTING METHODS: Although there is increasing acceptance and interest in SSIEEG, few studies have characterized the technical feasibility. Here, we demonstrate through modelling that scalp recordings from SSIEEG are comparable to that through an intact skull. CONCLUSION: The placement and simultaneous acquisition of scalp EEG during invasive monitoring through stereotactically inserted EEG electrodes is routinely performed at the Hospital for Sick Children. Scalp EEG recordings may assist with clinical interpretation. Burr holes in the skull layer did not discernibly alter EEG waveforms or topology.

A personalized earbud for non-invasive long-term EEG monitoring.

Objective. The primary objective of this study was to evaluate the reliability, comfort, and performance of a custom-fit, non-invasive long-term electrophysiologic headphone, known as Aware Hearable, for the ambulatory recording of brain activities. These recordings play a crucial role in diagnosing neurological disorders such as epilepsy and in studying neural dynamics during daily activities.Approach.The study uses commercial manufacturing processes common to the hearing aid industry, such as 3D scanning, computer-aided design modeling, and 3D printing. These processes enable the creation of the Aware Hearable with a personalized, custom-fit, thereby ensuring complete and consistent contact with the inner surfaces of the ear for high-quality data recordings. Additionally, the study employs a machine learning data analysis approach to validate the recordings produced by Aware Hearable, by comparing them to the gold standard intracranial electroencephalography recordings in epilepsy patients.Main results.The results indicate the potential of Aware Hearable to expedite the diagnosis of epilepsy by enabling extended periods of ambulatory recording.Significance.This offers significant reductions in burden to patients and their families. Furthermore, the device's utility may extend to a broader spectrum, making it suitable for other applications involving neurophysiological recordings in real-world settings.

Longitudinal point-of-care assessment of psychomotor vigilance in children in the epilepsy monitoring unit.

The epilepsy monitoring unit (EMU) is a complex and dynamic operational environment, where the cognitive and behavioural consequences of medical and environmental changes often go unnoticed. The psychomotor vigilance task (PVT) has been used to detect changes in cognition and behaviour in numerous contexts, including among astronauts on spaceflight missions, pilots, and commercial drivers. Here, we piloted serial point-of-care administration of the PVT in children undergoing invasive monitoring in the EMU. Seven children completed the PVT throughout their hospital admission and their performance was associated with daily seizure counts, interictal epileptiform discharges, number of antiseizure medications (ASMs) administered, and sleep quality metrics. Using mixed-effects models, we found that PVT reaction time and accuracy were adversely affected by greater number of ASMs and interictal epileptiform activity. We show that serial point-of-care PVT is simple and feasible in the EMU and may enable greater understanding of individual patient responses to medical and environmental alterations, inform clinical decision-making, and support quality-improvement and research initiatives.

Generalisability of epileptiform patterns across time and patients.

The complexity of localising the epileptogenic zone (EZ) contributes to surgical resection failures in achieving seizure freedom. The distinct patterns of epileptiform activity during interictal and ictal phases, varying across patients, often lead to suboptimal localisation using electroencephalography (EEG) features. We posed two key questions: whether neural signals reflecting epileptogenicity generalise from interictal to ictal time windows within each patient, and whether epileptiform patterns generalise across patients. Utilising an intracranial EEG dataset from 55 patients, we extracted a large battery of simple to complex features from stereo-EEG (SEEG) and electrocorticographic (ECoG) neural signals during interictal and ictal windows. Our features (n = 34) quantified many aspects of the signals including statistical moments, complexities, frequency-domain and cross-channel network attributes. Decision tree classifiers were then trained and tested on distinct time windows and patients to evaluate the generalisability of epileptogenic patterns across time and patients, respectively. Evidence strongly supported generalisability from interictal to ictal time windows across patients, particularly in signal power and high-frequency network-based features. Consistent patterns of epileptogenicity were observed across time windows within most patients, and signal features of epileptogenic regions generalised across patients, with higher generalisability in the ictal window. Signal complexity features were particularly contributory in cross-patient generalisation across patients. These findings offer insights into generalisable features of epileptic neural activity across time and patients, with implications for future automated approaches to supplement other EZ localisation methods.

A systematic review of the literature reporting on remote monitoring epileptic seizure detection devices.

BACKGROUND: Early detection and alert notification of an impending seizure for people with epilepsy have the potential to reduce Sudden Unexpected Death in Epilepsy (SUDEP). Current remote monitoring seizure detection devices for people with epilepsy are designed to support real-time monitoring of their vital health parameters linked to seizure alert notification. An understanding of the rapidly growing literature on remote seizure detection devices is essential to address the needs of people with epilepsy and their carers. AIM: This review aims to examine the technical characteristics, device performance, user preference, and effectiveness of remote monitoring seizure detection devices. METHODOLOGY: A systematic review referenced to PRISMA guidelines was used. RESULTS: A total of 1095 papers were identified from the initial search with 30 papers included in the review. Sixteen non-invasive remote monitoring seizure detection devices are currently available. Such seizure detection devices were found to have inbuilt intelligent sensor functionality to monitor electroencephalography, muscle movement, and accelerometer-based motion movement for detecting seizures remotely. Current challenges of these devices for people with epilepsy include skin irritation due to the type of patch electrode used and false alarm notifications, particularly during physical activity. The tight-fitted accelerometer-type devices are reported as uncomfortable from a wearability perspective for long-term monitoring. Also, continuous recording of physiological signals and triggering alert notifications significantly reduce the battery life of the devices. The literature highlights that 3.2 out of 5 people with epilepsy are not using seizure detection devices because of the cost and appearance of the device. CONCLUSION: Seizure detection devices can potentially reduce morbidity and mortality for people with epilepsy. Therefore, further collaboration of clinicians, technical experts, and researchers is needed for the future development of these devices. Finally, it is important to always take into consideration the expectations and requirements of people with epilepsy and their carers to facilitate the next generation of remote monitoring seizure detection devices.