Anticonvulsant effects of Paeonia daurica subsp. macrophylla root extracts in pentylenetetrazol-induced seizure models in mice / Efectos anticonvulsivos de Paeonia daurica subsp. macrophylla extractos de raíz en modelos de convulsiones inducidas por pentilentetrazol en ratones

Introduction: In the present study, anticonvulsant effects of aqueous extract (AE), hydro-alcoholic crude extract (HE), and its fractions (F-CHCl3, F-EtOAc, F-MeOH) of Paeonia daurica subsp. macrophylla (P. daurica ssp. macrophylla) root examined by using a pentylenetetrazol-induced model (PTZ) on mice. Methods: HE and its fractions as well as AE, in concentrations of (100, 200 and 400 mg/kg), valproate (Val) (100 and 200 mg/kg), and saline (negative control) (10 mg/kg) were injected intraperitoneally (i.p.) 30 min before PTZ (80 mg/kg, i.p.). The time taken before the onset of myoclonic convulsions (MC), MC duration, time taken before the onset of generalized tonic-clonic seizures (GTCS), the duration of GTCS, and the percentage of GTCS and mortality protection recorded. The plant's anticonvulsant mechanisms were assessed using flumazenil (5 mg/kg, i.p.) before AE (100, 200, and 400 mg/kg, i.p.) injection. GraphPad Prism software was used to compare the differences between various treatment groups with one-way analysis of variance (ANOVA) followed by Tukey–Krammer multiple comparison tests. Results: All the plant samples except F-EtOAc significantly delayed the onset and decreased the duration of PTZ-induced MCS and GTCS, and significantly reduced the GTCS and mortality rate. Pretreatment with flumazenil diminished the significant anticonvulsant effects of AE against PTZ-induced seizures. Conclusions: It can report that extract of P. daurica ssp. macrophylla might be a helpful guide for future studies in the treatment of epilepsy.(AU)

Introducción: Epilepsia es el término usado para un grupo de trastornos caracterizado por las convulsiones espontáneas recurrentes. Un estudio enfocado en los productos naturales de los recursos tradicionales ofrece ventajas significativas que se están utilizando de manera más amplia en modelos animales de epilepsia y candidatos a mayor desarrollo clínico y sus fracciones (F-CHCl3, F-EtOAc, F-MeOH) de Paeonia daurica subsp. macrophylla (P. daurica ssp. macrophylla) raíz examinada utilizando un modelo inducido por pentilentetrazol (PTZ) en ratones. Métodos: La maceración dinámica utilizada para extraer HE de la planta y técnica de cromatografía en columna de sílice utilizada para obtener F-CHCl3, F-EtOAc, así como fracciones de F-MeOH. La extracción de raíces secas se utilizó con agua destilada y se provocó AE. Las muestras de plantas (100, 200 y 400 mg/kg), valproato (Val) (100 y 200 mg/kg) y suero (control negativo) se inyectaron por vía intraperitoneal (ip) 30 min antes de PTZ (80 mg/kg, ip). El tiempo transcurrido antes del comienzo de convulsiones mioclónicas (MC), duración de las MC, tiempo transcurrido antes del comienzo de convulsiones tónico-clónicas generalizadas (GTCS), la duración de GTCS, así como el porcentaje de GTCS y protección contra la mortalidad registrada. Los mecanismos anticonvulsivos de planta fueron evaluados mediante el uso de flumazenil (5 mg/kg, ip) antes de AE (100, 200 y 400 mg/kg, ip) inyección. Se utilizaba el software GraphPad Prism® comparando las diferencias entre varios grupos de tratamiento con un análisis unilateral de variación (ANOVA) seguido por las pruebas de comparación múltiple de Tukey's Krammer. Resultados: Todas las muestras de plantas, excepto F-EtOAc, retrasaron de manera considerable el inicio, y disminuyeron la duración de PTZ inducidos por MCS y GTCS, y redujo significativamente el GTCS, así como la tasa de mortalidad...(AU)

Variantes normales de aspecto epileptiforme en el electroencefalograma. Revisión de la bibliografía e implicaciones clínicas / Normal variants with an epileptiform appearance in electroencephalograms. A literature review and clinical implications

Las variantes normales de aspecto epileptiforme, o variantes epileptiformes benignas, son un reto diagnóstico en la interpretación de los electroencefalogramas que requiere su conocimiento y una amplia experiencia por parte de los responsables del informe electroencefalográfico. Incluyen un grupo heterogéneo de hallazgos, algunos muy infrecuentes, que inicialmente se relacionaron con epilepsia y patologías neurológicas diversas. En la actualidad, la mayoría se consideran variantes sin significado patológico, y su sobreinterpretación habitualmente acarrea diagnósticos erróneos y tratamientos innecesarios. Los datos de prevalencia de estas variantes son muy diversos y proceden habitualmente de poblaciones seleccionadas, por lo que son difícilmente extrapolables a población sana. No obstante, estudios con electrodos invasivos y series más recientes vuelven a asociar algunas de estas variantes con epilepsia. Nuestro objetivo es revisar las características y la prevalencia de las principales variantes epileptiformes benignas y actualizar su significado clínico. (AU)

Pathophysiology, Diagnosis, Prognosis, and Prevention of Poststroke Epilepsy: Clinical and Research Implications.

Poststroke epilepsy (PSE) is associated with higher mortality and poor functional and cognitive outcomes in patients with stroke. With the remarkable development of acute stroke treatment, there is a growing number of survivors with PSE. Although approximately 10% of patients with stroke develop PSE, given the significant burden of stroke worldwide, PSE is a significant problem in stroke survivors. Therefore, the attention of health policymakers and significant funding are required to promote PSE prevention research. The current PSE definition includes unprovoked seizures occurring more than 7 days after stroke onset, given the high recurrence risks of seizures. However, the pathologic cascade of stroke is not uniform, indicating the need for a tissue-based approach rather than a time-based one to distinguish early seizures from late seizures. EEG is a commonly used tool in the diagnostic work-up of PSE. EEG findings during the acute phase of stroke can potentially stratify the risk of subsequent seizures and predict the development of poststroke epileptogenesis. Recent reports suggest that cortical superficial siderosis, which may be involved in epileptogenesis, is a promising marker for PSE. By incorporating such markers, future risk-scoring models could guide treatment strategies, particularly for the primary prophylaxis of PSE. To date, drugs that prevent poststroke epileptogenesis are lacking. The primary challenge involves the substantial cost burden due to the difficulty of reliably enrolling patients who develop PSE. There is, therefore, a critical need to determine reliable biomarkers for PSE. The goal is to be able to use them for trial enrichment and as a surrogate outcome measure for epileptogenesis. Moreover, seizure prophylaxis is essential to prevent functional and cognitive decline in stroke survivors. Further elucidation of factors that contribute to poststroke epileptogenesis is eagerly awaited. Meanwhile, the regimen of antiseizure medications should be based on individual cardiovascular risk, psychosomatic comorbidities, and concomitant medications. This review summarizes the current understanding of poststroke epileptogenesis, its risks, prognostic models, prophylaxis, and strategies for secondary prevention of seizures and suggests strategies to advance research on PSE.

Psychiatric implications of anti-seizure medications in epileptic population.

BACKGROUND AND OBJECTIVE: Epilepsy is a prevalent neurological disorder that affects a significant number of individuals globally. This condition is associated with a high occurrence of psychiatric comorbidities, which can significantly affect the quality of life of individuals affected. The aim of this study was to investigate the association between antiseizure therapies and the likelihood of psychiatric comorbidities in individuals with epilepsy. METHODOLOGY: Data for this study was gathered from the Neurology referral center in Islamabad, Pakistan. A standardized questionnaire was utilized to gather data from 120 individuals diagnosed with epilepsy. The survey consisted of inquiries regarding the management of seizures, the utilization of anti-seizure medications, and the presence of psychiatric comorbidities. The data was analyzed using the Statistical Package for the Social Sciences (SPSS). RESULTS: The findings indicated that individuals who were using multiple antiseizure medications had a notably higher likelihood of having psychiatric comorbidities in comparison to those who were on mono therapy (p = 0.010). suggests that patients with unsuccessful seizure control are more probable to have psychiatric comorbidities as compared to those with good seizure control (p = 0.029). CONCLUSION: To conclude poor seizure control and poly therapy are associated with increased risk of psychiatric comorbidities.

Expanding the clinical phenotype and genetic spectrum of GEMIN5 disorders: Early-infantile developmental and epileptic encephalopathies.

BACKGROUND: Several biallelic truncating and missense variants of the gem nuclear organelle-associated protein 5 (GEMIN5) gene have been reported to cause neurodevelopmental disorders characterized by cerebellar atrophy, intellectual disability, and motor dysfunction. However, the association between biallelic GEMIN5 variants and early-infantile developmental and epileptic encephalopathies (EIDEEs) has not been reported. PURPOSE: This study aimed to expand the phenotypic spectrum of GEMIN5 and explore the correlations between epilepsy and molecular sub-regional locations. METHODS: We performed whole-exome sequencing in two patients with EIDEE with unexplained etiologies. The damaging effects of variants were predicted using multiple in silico tools and modeling. All reported patients with GEMIN5 pathogenic variants and detailed neurological phenotypes were analyzed to evaluate the genotype-phenotype relationship. RESULTS: Novel biallelic GEMIN5 variants were identified in two unrelated female patients with EIDEE, including a frameshift variant (Hg19, chr5:154284147-154284148delCT: NM_015465: c.2551_c.2552delCT: p.(Leu851fs*30)), a nonsense mutation (Hg19, chr5:154299603-154299603delTinsAGA: NM_015465: c.1523delTinsAGA: p.(Leu508*)), and two missense variants (Hg19, chr5:154282663T > A: NM_015465: c.2705T > A: p.(Leu902Gln) and Hg19, chr5:154281002C > G: NM_015465: c.2911C > G: p.(Gln971Glu)), which were inherited from asymptomatic parents and predicted to be damaging or probably damaging using in silico tools. Except p.Leu508*, all these mutations are located in tetratricopeptide repeat (TPR) domain. Our two female patients presented with seizures less than 1 month after birth, followed by clusters of spasms. Brain magnetic resonance imaging suggests dysgenesis of the corpus callosum and cerebellar hypoplasia. Video electroencephalogram showed suppression-bursts. Through a literature review, we found 5 published papers reporting 48 patients with biallelic variants in GEMIN5. Eight of 48 patients have epilepsy, and 5 patients started before 1 year old, which reminds us of the relevance between GEMIN5 variants and EIDEE. Further analysis of the 49 GEMIN5 variants in those 50 patients demonstrated that variants in TPR-like domain or RBS domain were more likely to be associated with epilepsy. CONCLUSIONS: We found novel biallelic variants of GEMIN5 in two individuals with EIDEE and expanded the clinical phenotypes of GEMIN5 variants. It is suggested that the GEMIN5 gene should be added to the EIDEE gene panel to aid in the clinical diagnosis of EIDEE and to help determine patient prognosis.

The effects of leptin and cannabinoid CB1 receptor agonist/antagonist in cerebral tissues of epileptic rats.

OBJECTIVE: In this study, the effects of leptin, cannabinoid-1 (CB1) receptor agonist ACEA and antagonist AM251, and the interactions between leptin and CB1 receptor agonist/antagonist on oxidant and antioxidant enzymes in the cerebrum, cerebellum, and pedunculus cerebri tissue samples were investigated in the penicillin-induced epileptic model. METHODS: Male Wistar albino rats (n=56) were included in this study. In anesthetized animals, 500 IU penicillin-G potassium was injected into the cortex to induce epileptiform activity. Leptin (1 µg), ACEA (7.5 µg), AM251 (0.25 µg), and the combinations of the leptin+ACEA and leptin+AM251 were administered intracerebroventricularly (i.c.v.) after penicillin injections. Malondialdehyde (MDA), superoxide dismutase (SOD), and glutathione peroxidase (GPx) levels were measured in the cerebral tissue samples and plasma with the ELISA method. RESULTS: MDA levels increased, while SOD and GPx levels decreased after penicillin injection in the cerebrum and cerebellum. The efficacy of penicillin on SOD, MDA and GPx levels was further enhanced after leptin or AM251 injections. Whereas, ACEA decreased the MDA levels and increased GPx levels compared with the penicillin group. Administration of AM251+leptin did not change any oxidation parameter compared with the AM251. Furthermore, co-administration of ACEA and leptin significantly increased oxidative stress compared with the ACEA-treated group by increasing MDA and decreasing GPx levels. CONCLUSION: It was concluded that leptin reversed the effect of ACEA on oxidative stress. Co-administration of AM251 and leptin did not change oxidative stress compared with the AM251-treated group suggesting AM251 and leptin affect oxidative stress using the same pathways.

Antiseizure adverse drug reaction and associated factors among epileptic patients at Jimma Medical Center: a prospective observational study.

A growing body of evidence suggests that adverse drug reactions (ADRs) are a major cause of morbidity and mortality in the healthcare system. Fifteen to twenty-five percent of patients with epilepsy discontinued antiseizure drugs (ASDs) within 6 months of therapy owing to intolerable adverse drug reactions. In Ethiopia, the prevalence of antiseizure adverse drug reactions and associated factors was not extensively conducted in advanced settings like Jimma Medical Centers. Hence, the objective of this study is to assess patterns of adverse drug reactions and associated factors among ambulatory epileptic patients at tertiary hospitals in Ethiopia. A hospital-based prospective observational study was spanned for 1 year. Two hundred ninety patients were consecutively recruited into the study from all epileptic patients attending the ambulatory clinic. Relevant data were collected through patient interviews and medical chart reviews. The causality assessment was done by using the Naranjo Probability Scale. Epi-Data manager version 4.6.0.4 was used for data entry and statistical analysis was performed by Statistical Package for Social Science version 25.0 (SPSS). Stepwise backward logistic regression analysis was done to identify factors that increase the risk of antiseizure adverse drug reactions. The mean (± SD) age of the participants were 29.91(± 11.26) years. The overall prevalence of ADR was 33.8% (95% CI 29.2-39.9%). A total of 110 adverse drug reactions were identified among 98 patients with an average of 1.12 per patient. ADRs were frequently reported with phenobarbital (52.04%) and phenytoin (34.70%). The commonly identified adverse drug reactions were epigastric pain (27.55%) and central nervous system drowsiness (23.46%). Comorbidity (AOR = 5.91, 95% CI (2.14-16.32), seizure-free period of fewer than 2 years (AOR = 1.94, 95% CI (1.18-3.19), and polytherapy (AOR = 1.35, 95% CI (1.80-2.26) were significantly associated with adverse drug reactions. This trial had a comparatively high percentage of adverse medication reactions. Adverse medication reactions were more common in patients with polytherapy, comorbidities, and seizure-free durations less than two years. Therefore, medical practitioners should advise patients who exhibit these traits on how to reduce or avoid bad drug responses or provide comfort in the event of small incidents.

Prenatal Exposure to Valproic Acid Across Various Indications for Use.

Importance: Teratogenic outcomes associated with valproic acid use represent a substantial concern for persons of childbearing age. Regulatory agencies worldwide have enhanced warnings or implemented risk minimization programs to reduce exposure during pregnancy. Objectives: To determine pregnancy rates during valproic acid use and concomitant contraception use across indications. Design, Setting, and Participants: This retrospective cohort study used data from the Merative MarketScan commercial claims databases from January 1, 2005, to December 31, 2020, to identify female patients aged 12 to 44 years who initiated valproic acid treatment and had continuous insurance enrollment 6 months before initiation and 9 months after treatment end. A treatment episode included consecutive prescription fills that occurred within 7 days from the end of the days' supply of the previous dispensing. Data were analyzed from March 1 to September 10, 2023. Main Outcomes and Measures: Treatment episodes were categorized by inferred indication using diagnoses preceding treatment initiation, including epilepsy, migraine or headache, mood disorders, and unknown or off-label uses. Pregnancy incidence rate ratios (IRRs) were calculated and were adjusted for age and calendar year. Contraceptive use (prescription contraceptives, intrauterine devices, and implants) during treatment was examined. Results: The cohort included 165 772 valproic acid treatment episodes among 69 390 women (mean [SD] age, 29.8 [10.0] years). Mood disorders (42.5%) were the most common indication, followed by migraine or headache (20.1%), with epilepsy playing a minor role (14.9%). Pregnancy incidence rates during valproic acid use remained unchanged, with a rate of 1.74 (95% CI, 1.14-2.53) per 100 person-years in 2005 and a rate of 1.90 (95% CI, 1.16-3.12) per 100 person-years in 2019. Compared with epilepsy, pregnancy rates were more than double for mood disorder (IRR, 2.16 [95% CI, 1.93-2.42]) and migraine or headache (IRR, 2.01 [95% CI, 1.92-2.09]). Few treatment episodes coincided with contraceptive use (37 012 [22.3%]), and oral dosage forms were the most common (27 069 [73.1%]). Conclusions and Relevance: In this cohort study of patients of childbearing age who used valproic acid, pregnancy rates during valproic acid use did not decrease despite enhanced US Food and Drug Administration safety communications, and contraception use remained low. Patients with migraine and mood disorders accounted for the largest proportion of valproic acid use and had the highest pregnancy rates, while patients with epilepsy had the lowest. These findings suggest a need to enhance efforts to mitigate prenatal exposure to valproic acid, especially for indications where the risk of use during pregnancy outweighs the benefit.

Performance investigation of epilepsy detection from noisy EEG signals using base-2-meta stacking classifier.

Epilepsy is a chronic neurological disease, characterized by spontaneous, unprovoked, recurrent seizures that may lead to long-term disability and premature death. Despite significant efforts made to improve epilepsy detection clinically and pre-clinically, the pervasive presence of noise in EEG signals continues to pose substantial challenges to their effective application. In addition, discriminant features for epilepsy detection have not been investigated yet. The objective of this study is to develop a hybrid model for epilepsy detection from noisy and fragmented EEG signals. We hypothesized that a hybrid model could surpass existing single models in epilepsy detection. Our approach involves manual noise rejection and a novel statistical channel selection technique to detect epilepsy even from noisy EEG signals. Our proposed Base-2-Meta stacking classifier achieved notable accuracy (0.98 ± 0.05), precision (0.98 ± 0.07), recall (0.98 ± 0.05), and F1 score (0.98 ± 0.04) even with noisy 5-s segmented EEG signals. Application of our approach to the specific problem like detection of epilepsy from noisy and fragmented EEG data reveals a performance that is not only superior to others, but also is translationally relevant, highlighting its potential application in a clinic setting, where EEG signals are often noisy or scanty. Our proposed metric DF-A (Discriminant feature-accuracy), for the first time, identified the most discriminant feature with models that give A accuracy or above (A = 95 used in this study). This groundbreaking approach allows for detecting discriminant features and can be used as potential electrographic biomarkers in epilepsy detection research. Moreover, our study introduces innovative insights into the understanding of these features, epilepsy detection, and cross-validation, markedly improving epilepsy detection in ways previously unavailable.

Ketogenic Diet in the Treatment of Epilepsy.

Epilepsy is one of the most disabling neurological diseases. Despite proper pharmacotherapy and the availability of 2nd and 3rd generation antiepileptic drugs, deep brain stimulation, and surgery, up to 30-40% of epilepsy patients remain drug-resistant. Consequences of this phenomenon include not only decreased a quality of life, and cognitive, behavioral, and personal disorders, but also an increased risk of death, i.e., in the mechanism of sudden unexpected death in epilepsy patients (SUDEP). The main goals of epilepsy treatment include three basic issues: achieving the best possible seizure control, avoiding the undesired effects of treatment, and maintaining/improving the quality of patients' lives. Therefore, numerous attempts are made to offer alternative treatments for drug-resistant seizures, an example of which is the ketogenic diet. It is a long-known but rarely used dietary therapy for intractable seizures. One of the reasons for this is the unpalatability of the classic ketogenic diet, which reduces patient compliance and adherence rates. However, its antiseizure effects are often considered to be worth the effort. Until recently, the diet was considered the last-resort treatment. Currently, it is believed that a ketogenic diet should be used much earlier in patients with well-defined indications. In correctly qualified patients, seizure activity may be reduced by over 90% or even abolished for long periods after the diet is stopped. A ketogenic diet can be used in all age groups, although most of the available literature addresses pediatric epilepsy. In this article, we focus on the mechanisms of action, effectiveness, and adverse effects of different variants of the ketogenic diet, including its classic version, a medium-chain triglyceride diet, a modified Atkins diet, and a low glycemic index treatment.

Evaluation of the Effect of Parenting Style and Parental Mealtime Actions on the Eating Behavior of Children with Epilepsy.

BACKGROUND: Research on the interaction of parenting style, parents' mealtime behaviors, and children's eating behavior in the presence of chronic disease is limited. This study aimed to investigate the impact of parenting style and parental mealtime actions on the eating behavior of children with epilepsy. METHODS: Thirty-one children with epilepsy, thirty-one healthy children (aged 4-9 years), and their parents were included. The Multidimensional Assessment of Parenting Scale (MAPS), Parent Mealtime Action Scale, Children's Eating Behavior Questionnaire, and Healthy Eating Index (HEI)-2015 were applied. The MAPS, HEI-2015 scores, and body mass index for age Z scores were similar in both groups (p > 0.05). In the epilepsy group, the food approach behavior score was higher, and positive correlations were noted between broadband negative parenting and food approach behavior, and the HEI-2015 score and broadband positive parenting (p < 0.05). Regression analysis showed that broadband negative parenting and snack modeling increased the food approach behavior in the epilepsy group. Owing to the chronic disease, the effects of parent-child interaction on the child's eating behavior in the epilepsy group differed from those of healthy children reported in the literature.

Differentiating Epileptic and Psychogenic Non-Epileptic Seizures Using Machine Learning Analysis of EEG Plot Images.

The treatment of epilepsy, the second most common chronic neurological disorder, is often complicated by the failure of patients to respond to medication. Treatment failure with anti-seizure medications is often due to the presence of non-epileptic seizures. Distinguishing non-epileptic from epileptic seizures requires an expensive and time-consuming analysis of electroencephalograms (EEGs) recorded in an epilepsy monitoring unit. Machine learning algorithms have been used to detect seizures from EEG, typically using EEG waveform analysis. We employed an alternative approach, using a convolutional neural network (CNN) with transfer learning using MobileNetV2 to emulate the real-world visual analysis of EEG images by epileptologists. A total of 5359 EEG waveform plot images from 107 adult subjects across two epilepsy monitoring units in separate medical facilities were divided into epileptic and non-epileptic groups for training and cross-validation of the CNN. The model achieved an accuracy of 86.9% (Area Under the Curve, AUC 0.92) at the site where training data were extracted and an accuracy of 87.3% (AUC 0.94) at the other site whose data were only used for validation. This investigation demonstrates the high accuracy achievable with CNN analysis of EEG plot images and the robustness of this approach across EEG visualization software, laying the groundwork for further subclassification of seizures using similar approaches in a clinical setting.

Calibrating Deep Learning Classifiers for Patient-Independent Electroencephalogram Seizure Forecasting.

The recent scientific literature abounds in proposals of seizure forecasting methods that exploit machine learning to automatically analyze electroencephalogram (EEG) signals. Deep learning algorithms seem to achieve a particularly remarkable performance, suggesting that the implementation of clinical devices for seizure prediction might be within reach. However, most of the research evaluated the robustness of automatic forecasting methods through randomized cross-validation techniques, while clinical applications require much more stringent validation based on patient-independent testing. In this study, we show that automatic seizure forecasting can be performed, to some extent, even on independent patients who have never been seen during the training phase, thanks to the implementation of a simple calibration pipeline that can fine-tune deep learning models, even on a single epileptic event recorded from a new patient. We evaluate our calibration procedure using two datasets containing EEG signals recorded from a large cohort of epileptic subjects, demonstrating that the forecast accuracy of deep learning methods can increase on average by more than 20%, and that performance improves systematically in all independent patients. We further show that our calibration procedure works best for deep learning models, but can also be successfully applied to machine learning algorithms based on engineered signal features. Although our method still requires at least one epileptic event per patient to calibrate the forecasting model, we conclude that focusing on realistic validation methods allows to more reliably compare different machine learning approaches for seizure prediction, enabling the implementation of robust and effective forecasting systems that can be used in daily healthcare practice.

Reproductive and fetal outcomes in women with epilepsy: a systematic review and meta-analysis.

OBJECTIVE: Although early evidence shows that epilepsy can increase the risks of adverse pregnancy, some outcomes are still debatable. We performed a systematic review and meta-analysis to explore the effects of maternal and fetal adverse outcomes in pregnant women with epilepsy. METHODS: PubMed, Embase, Cochrane, and Web of Science were employed to collect studies that investigated the potential risk of obstetric complications during the antenatal, intrapartum, or postnatal period, as well as any neonatal complications. The search was conducted from inception to November 16, 2022. The Newcastle-Ottawa Scale (NOS) was used to assess the quality of the included original studies. The odds ratio (OR) values were extracted after adjusting for confounders to measure the relationship between pregnant women with epilepsy and adverse maternal or fetal outcomes. The protocol for this systematic review is registered with PROSPERO ID CRD42023391539. RESULTS: Of 35 articles identified, there were 142,577 mothers with epilepsy and 34,381,373 mothers without epilepsy. Our study revealed a significant association between pregnant women with epilepsy (PWWE) and the incidence of cesarean section, preeclampsia/eclampsia, gestational hypertension, induction of labor, gestational diabetes and postpartum hemorrhage compared with those without epilepsy. Regarding newborns outcomes, PWWE versus those without epilepsy had increased odds of preterm birth, small for gestational age, low birth weight (<2500 g), and congenital malformations, fetal distress. The odds of operative vaginal delivery, newborn mortality, and Apgar (≤ 7) were similar between PWWE and healthy women. CONCLUSION: Pregnant women affected by epilepsy encounter a higher risk of adverse obstetric outcomes and fetal complications. Therefore, it is crucial to develop appropriate prevention and intervention strategies prior to or during pregnancy to minimize the negative impacts of epilepsy on maternal and fetal health.

EEG power spectra parameterization and adaptive channel selection towards semi-supervised seizure prediction.

BACKGROUND: The seizure prediction algorithms have demonstrated their potential in mitigating epilepsy risks by detecting the pre-ictal state using ongoing electroencephalogram (EEG) signals. However, most of them require high-density EEG, which is burdensome to the patients for daily monitoring. Moreover, prevailing seizure models require extensive training with significant labeled data which is very time-consuming and demanding for the epileptologists. METHOD: To address these challenges, here we propose an adaptive channel selection strategy and a semi-supervised deep learning model respectively to reduce the number of EEG channels and to limit the amount of labeled data required for accurate seizure prediction. Our channel selection module is centered on features from EEG power spectra parameterization that precisely characterize the epileptic activities to identify the seizure-associated channels for each patient. The semi-supervised model integrates generative adversarial networks and bidirectional long short-term memory networks to enhance seizure prediction. RESULTS: Our approach is evaluated on the CHB-MIT and Siena epilepsy datasets. With utilizing only 4 channels, the method demonstrates outstanding performance with an AUC of 93.15% on the CHB-MIT dataset and an AUC of 88.98% on the Siena dataset. Experimental results also demonstrate that our selection approach reduces the model parameters and training time. CONCLUSIONS: Adaptive channel selection coupled with semi-supervised learning can offer the possible bases for a light weight and computationally efficient seizure prediction system, making the daily monitoring practical to improve patients' quality of life.