Search results for: patients with epilepsy

Authors: Susan Amirsalari, Azime Khosrinejad, Elham Rahimian

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Objective: Epilepsy is a common brain disorder characterized by a persistent tendency to develop in neurological, cognitive, and psychological contents. Magnetic Resonance Imaging (MRI) is a neuroimaging test facilitating the detection of structural epileptogenic lesions. This study aimed to compare the MRI findings between patients with intractable and drug-responsive epilepsy. Material & methods: This case-control study was conducted from 2007 to 2019. The research population encompassed all 1-16- year-old patients with intractable epilepsy referred to the Shafa Neuroscience Center (n=72) (a case group) and drug-responsive patients referred to the pediatric neurology clinic of Baqiyatallah Hospital (a control group). Results: There were 72 (23.5%) patients in the intractable epilepsy group and 200 (76.5%) patients in the drug-responsive group. The participants' mean age was 6.70 ±4.13 years, and there were 126 males and 106 females in this study Normal brain MRI was noticed in 21 (29.16%) patients in the case group and 184 (92.46%) patients in the control group. Neuronal migration disorder (NMD)was also exhibited in 7 (9.72%) patients in the case group and no patient in the control group. There were hippocampal abnormalities and focal lesions (mass, dysplasia, etc.) in 10 (13.88%) patients in the case group and only 1 (0.05%) patient in the control group. Gliosis and porencephalic cysts were presented in 3 (4.16%) patients in the case group and no patient in the control group. Cerebral and cerebellar atrophy was revealed in 8 (11.11%) patients in the case group and 4 (2.01%) patients in the control group. Corpus callosum agenesis, hydrocephalus, brain malacia, and developmental cyst were more frequent in the case group; however, the difference between the groups was not significant. Conclusion: The MRI findings such as hippocampal abnormalities, focal lesions (mass, dysplasia), NMD, porencephalic cysts, gliosis, and atrophy are significantly more frequent in children with intractable epilepsy than in those with drug-responsive epilepsy.

Keywords: magnetic resonance imaging, intractable epilepsy, drug responsive epilepsy, neuronal migrational disorder

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Authors: Hamood Al Kiyumi, Salim Al Huseini, Khalid Al Risi, Hassan Mirza, Amira Al Hosni, Sanjay Jaju, Asaad Al Habsi

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Objectives: The aim of this study was to assess the proportion of depressive symptoms along with demographic data in children diagnosed with epilepsy in a tertiary care institution in Oman. Methods: This cross-sectional study was conducted between June 2016 and August 2018. We have included 75 children with age group from five to 12 years old, attending epilepsy clinic at Sultan Qaboos University Hospital who were diagnosed with epilepsy and already on treatment. Patients were excluded if they have mental retardation. Validated Depression Scale for Children (CES-DC) questionnaire was utilized to assess the level of depressive symptoms among children. In addition, we have looked at associated factors including seizure status in the last three months, compliance with antiepileptic medications, type of epilepsy, and number of antiepileptic medications. Results: In this study, we found that depressive symptoms were present in 39 (52%) of patients. We also found that 96% of the patients were compliant to medications. In addition, seizure was present in the last three months in 48% of the sample studies. There was no statistically significant association between any of the studied variables and depression. Conclusions: Although depression is highly prevalent in children with epilepsy, this study did not find any significant association between the CES-DC scores and the studied factors.

Keywords: depression, children, epilepsy, Oman

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Authors: Sarah E. Casey

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Epilepsy is the most common neurological disorder in children and approximately one-third of children with epilepsy have seizures that are uncontrolled on anticonvulsants alone. Cannabidiol is shown to be an effective treatment at reducing the amount of breakthrough seizures experienced by children with drug resistant epilepsy. Improvements in quality of life and overall condition were noted during cannabidiol treatment. Adverse side effects were experienced and were generally mild to moderate in nature. Additional double-blind, controlled studies with a more diverse sample population and standardized dosing are needed to ensure the efficacy and safety of cannabidiol use in children with drug resistant epilepsy.

Keywords: cannabis, drug resistant epilepsy, children, epilepsy

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Authors: Mohammed E. Ibrahim, Baraa A. Taha, Kamil M. A. Shabban

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Background: Epilepsy is a common but widely misunderstood illness. Consequently, patients with epilepsy suffer from considerable stigmatization in society. This social stigma and discrimination often cause more suffering for the patients than the disease itself. Since very few studies have explored the misperceptions about epilepsy among university students in Sudan, it is not possible to provide focused intervention aimed at eliminating this discrimination. Methods: A cross-sectional study was applied among the first-year medical students at the University of Khartoum between December (2020) and February (2021). A 29-item standardized questionnaire was self-administered by 198 students (out of 320) who agreed to participate in this study. Google form was the tool used to collect the data. The data were analyzed using the Statistical Package for Social Science software version 26. Result: Overall, the results indicate a negative trend in knowledge and attitude toward epilepsy. The vast majority of the respondents (84.8%) have read or heard about epilepsy, while 43.9% had seen someone with epilepsy. Only 7.5% of the participants reported that epilepsy is contagious, whereas 43.4% of them think that epilepsy is a psychological disorder. About 62.2% of students think head/birth trauma is a cause of epilepsy. On the other side, about 15.7% and 5.1% believed that evil spirits and punishment from god can also be a possible cause of epilepsy; we found these false beliefs are more common in participants from rural areas (p-value < 0.05). In regard to attitude, 19.7% of students thought that it is inappropriate for a patient with epilepsy to have a child. This attitude correlates with the mother’s education as the percentage is higher for those who have lower mother’s education (through secondary school education and below) (p < 0.05). The majority of Our participant knew that some people with epilepsy need life-long drug treatment; this belief was found to be more common in females than their counterparts(p < 0.05). . Finally, most of the respondents (93.9%) thought that a child with epilepsy Can be successful in a normal class. This belief is four-time as common in participants whose mothers have higher education (through university education and above) compared with corresponding respondents (p < 0.05). Conclusion: This study concludes that students' knowledge about epilepsy is limited and requires immediate intervention through educational campaigns to develop a well-informed and tolerant community.

Keywords: epilepsy, awareness, attitude, university students, Sudan

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Authors: Timea Harmath-Tánczos

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Drug-resistant epilepsy (DRE) is defined as the persistence of seizures despite at least two syndrome-adapted antiseizure drugs (ASD) used at efficacious daily doses. About a third of patients with epilepsy suffer from drug resistance. Cognitive assessment has a crucial role in the diagnosis and clinical management of epilepsy. Previous studies have addressed the clinical targets and indications for measuring neuropsychological functions; best to our knowledge, no studies have examined it in a Hungarian therapy-resistant population. To fill this gap, we investigated the Hungarian diagnostic protocol between 18 and 65 years of age. This study aimed to describe and analyze neuropsychological functions in patients with drug-resistant epilepsy and identify factors associated with neuropsychology deficits. We perform a prospective case-control study comparing neuropsychological performances in 50 adult patients and 50 healthy individuals between March 2023 and July 2023. Neuropsychological functions were examined in both patients and controls using a full set of specific tests (general performance level, motor functions, attention, executive facts., verbal and visual memory, language, and visual-spatial functions). Potential risk factors for neuropsychological deficit were assessed in the patient group using a multivariate analysis. The two groups did not differ in age, sex, dominant hand and level of education. Compared with the control group, patients with drug-resistant epilepsy showed worse performance on motor functions and visuospatial memory, sustained attention, inhibition and verbal memory. Neuropsychological deficits could therefore be systematically detected in patients with drug-resistant epilepsy in order to provide neuropsychological therapy and improve quality of life. The analysis of the classical and complex indices of the special neuropsychological tasks presented in the presentation can help in the investigation of normal and disrupted memory and executive functions in the DRE.

Keywords: drug-resistant epilepsy, Hungarian diagnostic protocol, memory, executive functions, cognitive neuropsychology

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Authors: Nyimas Annissa Mutiara Andini

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There are 325,000 children younger than age 15 in the U.S. have epilepsy. In Indonesia, 40% of 3,5 millions cases of epilepsy happens in children. The most common type of epilepsy, which affects 6 out of 10 people with the disorder, is called idiopathic epilepsy and which has no identifiable cause. One of the most commonly used medications in the treatment of this childhood epilepsy is sodium valproate. Administration of sodium valproat in children has a problem to fail. Nearly 60% of pediatric patients known were mildly, moderately, or severely non-adherent with therapy during the first six months of treatment. Many parents or caregiver took far less medication than prescribed, and the treatment-adherence pattern for the majority of patients was established during the first month of treatment. 42% of the patients were almost always given their medications as prescribed but 13% had very poor adherence even in the early weeks and months of treatment. About 7% of patients initially gave the medication correctly 90% of the time, but adherence dropped to around 20% within six months of starting treatment. Over the six months of observation, the total missing of administration is about four out of 14 doses in any given week. This fail can cause the epilepsy to relapse. Whereas, current reported epilepsy disorder were significantly more likely than those never diagnosed to experience depression (8% vs 2%), anxiety (17% vs 3%), attention-deficit/hyperactivity disorder (23% vs 6%), developmental delay (51% vs 3%), autism/autism spectrum disorder (16% vs 1%), and headaches (14% vs 5%) (all P< 0.05). They had a greater risk of limitation in the ability to do things (relative risk: 9.22; 95% CI: 7.56–11.24), repeating a school grade (relative risk: 2.59; CI: 1.52–4.40), and potentially having unmet medical and mental health needs. In the other side, technology can help to make our life easier. One of the technology, that we can use is a mobile application. A mobile app is a software program we can download and access directly using our phone. Indonesians are highly mobile centric. They use, on average, 6.7 applications over a 30 day period. This paper is aimed to describe an application that could help to control a sodium valproat administration in children; we call it as D-Epi app. D-Epi app is a downloadable application that can help parents or caregiver alert by a timer-related application to warn whether it is the time to administer the sodium valproat. It works not only as a standard alarm, but also inform important information about the drug and emergency stuffs to do to children with epilepsy. This application could help parents and caregiver to take care a child with epilepsy in Indonesia.

Keywords: application, children, D-Epi, epilepsy

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Authors: C. Puangsawat, C. Limotai, P. Srikhachin

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Bio-psycho-social caring was required for promoting the quality of life of the patients with epilepsy (PWE), despite controlled seizures. Multifaceted issues emerge at the epilepsy clinic. Unpredicted seizures, antiepileptic drug compliance problems/adverse effects, psychiatric, and social problems are all needed to be explored and managed. The Nursing Service System (NSS) at the tertiary epilepsy clinic (TEC) was consequently developed for improving the clinical care for PWE. Case manager concept was integrated as the framework guiding the processes and strategies used for developing the NSS as well as the roles of the multidisciplinary team at the clinic. This study aimed to report the outcomes of the developed NSS integrated case manager concept. The processes of our developed NSS program included 1) screening for patient’s problems using questionnaire prior to seeing epileptologists i.e., assessing the patient’s risk to develop acute seizures at the clinic, issues related to medication use, and uncovered psychiatric and social problems; and 2) assigning the patients at risk to be evaluated and managed by appropriate team. Nurses specializing in epilepsy in coordination with the multidisciplinary team implemented the NSS to promote coordinated work among the team which consists of epileptologists, nurses, pharmacists, psychologists, and social workers. Determination of the role of each person and their responsibilities along with joint care plan were clearly established. One year after implementation, the rate of acute seizure occurrence at the clinic was decreased, and satisfactory feedback from the patients was received. In order to achieve an optimal goal to promote self-management behaviors in PWE, continuing the NSS and systematic assessment of its effectiveness is required.

Keywords: case manager concept, nursing service system, patients with epilepsy, quality of life

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Authors: Usha Barahmand, Ruhollah Heydari Sheikh Ahmad, Sara Alaie Khoraem

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Introduction: The association of epilepsy with several psychological disorders and reduced quality of life has long been recognized. The present study aimed at comparing the personality profiles, quality of life and symptomatology of anxiety and depression in patients with epilepsy and healthy controls. Materials and Methods: Forty seven patients (29 men and 18 women) with diagnosed epilepsy participated in this study. Forty seven healthy controls who matched the patients in age and gender were also recruited. The participants’ personality and psychological profiles were assessed using the Depression, Anxiety, and Stress Scale (DASS-21), the Short-Form Health Survey (SF-36) and the HEXACO Personality Inventory (HEXACO-PI). Scoring algorithms were applied to the SF-36 produce the physical and mental component scores (PCS and MCS). Results: There were statistically significant differences in the total SF-36 score, anxiety, depression and stress scores of the DASS-21 between patients and controls. Anxiety, stress and depression scores significantly correlated inversely with the PCS and MCS. Data analysis showed that females had higher depression scores than males in both patients and controls, while males in both groups scored higher on stress. Patients’ personality scores were also different from those reported by controls on emotional, agreeableness and extroversion. Patients scored higher on emotionality, and lower on agreeableness and extraversion. Patients also scored lower on indices of quality of life. Regression analysis revealed that emotionality, anxiety, stress and MCS accounted for a significant proportion of the variance in severity of epileptic seizures. Conclusion: Stressful situations and psychological conditions as well as the personality trait of neuroticism were related to the occurrence of recurrent epileptic seizures.

Keywords: anxiety, depression, epilepsy, neuroticism, personality, quality of life, stress

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Authors: Wei-Han Wang, Hsiang-Yu Yu, Mau-Sun Hua

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Theory of Mind (ToM) refers to the ability to infer another’s mental state. With appropriate ToM, one can behave well in social interactions. A growing body of evidence has demonstrated that patients with temporal lobe epilepsy (TLE) may have damaged ToM due to impact on regions of the underlying neural network of ToM. However, the question of whether there is cerebral laterality for ToM functions remains open. This study aimed to examine whether there is cerebral lateralization for ToM abilities in TLE patients. Sixty-seven adult TLE patients and 30 matched healthy controls (HC) were recruited. Patients were classified into right (RTLE), left (LTLE), and bilateral (BTLE) TLE groups on the basis of a consensus panel review of their seizure semiology, EEG findings, and brain imaging results. All participants completed an intellectual test and four tasks measuring basic and advanced ToM. The results showed that, on all ToM tasks; (1)each patient group performed worse than HC; (2)there were no significant differences between LTLE and RTLE groups; (3)the BTLE group performed the worst. It appears that the neural network responsible for ToM is distributed evenly between the cerebral hemispheres.

Keywords: cerebral lateralization, social cognition, temporal lobe epilepsy, theory of mind

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Authors: Kumar Ashwini, Nayak C. Vinod

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Sudden unexpected death in epilepsy (SUDEP) is a rarity. Each year, about one in 150 epileptics, whose seizures are not controlled, may die of SUDEP. It is a leading cause of death in young adults with uncontrolled seizures. Understanding the genetic basis for SUDEP, is crucial given that the rate of sudden death in epilepsy patients is 20 fold that of the general population. We encountered one such case of a young male, a known epileptic, who was brought dead after a sudden collapse. We hereby present a poster discussing the autopsy findings of this case and also highlighting the importance of understanding the genetic basis of SUDEP.

Keywords: sudden death, epilepsy, genetic, autopsy

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Authors: Arthy Vinayakam, Emilda Judith Ezhil Rajan

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Adolescents with epilepsy living in developing country like India face many difficulties on stigma towards the disease. The psychological wellbeing of adolescents who are living with epilepsy has a varied influence on their daily activities and decision-making. Parental involvement with adolescents has always been a subject of caution. The dynamics in adolescents with epilepsy is much varied as their parental aspects has been known to have an impact on their education, socialization and wellbeing. The current study aims to identify the effect of parental styles, how they tend to effect the perception of self-concept that relate to the stigma in adolescents with epilepsy. A sample of 30 adolescents with epilepsy and their parents were taken; a control group of 30 adolescents and their parents were also taken. The General Health Questionnaire -12 was used as a screening for both groups to be included in the study. Parents were evaluated with Parenting Practices Questionnaire (PPQ). Adolescents were administered the Epilepsy Stigma Scale (ESS), Rosenberg Self-esteem Scale (RSS) and Adolescent Wellbeing Scale (AWS). Descriptive statistics was used to analyze the data. The findings of the study highlight the challenges of both parent and their influence on adolescent’s wellbeing. The findings also establish the impact of parenting style on the stigma in adolescents having epilepsy and how this influences their self-concept whereby their emotional strength.

Keywords: epilepsy, parenting style, stigma, wellbeing

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Authors: Abdelrahman A. Ramzy, Bassel S. Abdallah, Mohamed E. Bahgat, Sarah M. Abdelkader, Sherif H. ElGohary

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Attention-Deficit/Hyperactivity Disorder (ADHD), epilepsy, and autism affect millions of children worldwide, many of which are undiagnosed despite the fact that all of these disorders are detectable in early childhood. Late diagnosis can cause severe problems due to the late treatment and to the misconceptions and lack of awareness as a whole towards these disorders. Moreover, electroencephalography (EEG) has played a vital role in the assessment of neural function in children. Therefore, quantitative EEG measurement will be utilized as a tool for use in the evaluation of patients who may have ADHD, epilepsy, and autism. We propose a screening tool that uses EEG signals and machine learning algorithms to detect these disorders at an early age in an automated manner. The proposed classifiers used with epilepsy as a step taken for the work done so far, provided an accuracy of approximately 97% using SVM, Naïve Bayes and Decision tree, while 98% using KNN, which gives hope for the work yet to be conducted.

Keywords: ADHD, autism, epilepsy, EEG, SVM

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Authors: Mona Hejazi, Ali Motie Nasrabadi

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Epilepsy is a persistent neurological disorder that affects more than 50 million people worldwide. Hence, there is a necessity to introduce an efficient prediction model for making a correct diagnosis of the epileptic seizure and accurate prediction of its type. In this study we consider how the Effective Connectivity (EC) patterns obtained from intracranial Electroencephalographic (EEG) recordings reveal information about the dynamics of the epileptic brain and can be used to predict imminent seizures, as this will enable the patients (and caregivers) to take appropriate precautions. We use this definition because we believe that effective connectivity near seizures begin to change, so we can predict seizures according to this feature. Results are reported on the standard Freiburg EEG dataset which contains data from 21 patients suffering from medically intractable focal epilepsy. Six channels of EEG from each patients are considered and effective connectivity using Directed Transfer Function (DTF) and Granger Causality (GC) methods is estimated. We concentrate on effective connectivity standard deviation over time and feature changes in five brain frequency sub-bands (Alpha, Beta, Theta, Delta, and Gamma) are compared. The performance obtained for the proposed scheme in predicting seizures is: average prediction time is 50 minutes before seizure onset, the maximum sensitivity is approximate ~80% and the false positive rate is 0.33 FP/h. DTF method is more acceptable to predict epileptic seizures and generally we can observe that the greater results are in gamma and beta sub-bands. The research of this paper is significantly helpful for clinical applications, especially for the exploitation of online portable devices.

Keywords: effective connectivity, Granger causality, directed transfer function, epilepsy seizure prediction, EEG

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Authors: I-Han Hsiao, Chun-Ping Huang, Ching-Liang Hsieh, Yi-Wen Lin

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Epilepsy is chronic brain disorder that results in the sporadic occurrence of spontaneous seizures in the temporal lobe, cerebral cortex, and hippocampus. Clinical antiepileptic medicines are often ineffective or little benefits in the small amount of patients and usually initiate severe side effects. This inflammation contributes to enhanced neuronal excitability and the onset of epilepsy. Auricular electric-stimulation (AES) can increase parasympathetic activity and stimulate the solitary tract nucleus to induce the cholinergic anti-inflammatory pathway. Furthermore, it may be a therapeutic strategy for the treatment of epilepsy. In the present study, we want to investigate the effects of AES on inflammatory mediators in kainic acid (KA)-induced epileptic seizure rats. Experimental KA injection increased expression of TLR-2 pathway associated inflammatory mediators, were further reduced by either 2Hz or 15 Hz AES in the prefrontal cortex, hippocampus, and somatosensory cortex. We suggest that AES can successfully control the epileptic seizure by down-regulation of inflammation signaling pathway.

Keywords: auricular electric-stimulation, epileptic seizures, anti-inflammation

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Authors: Eric Jacob Bacon, Chaoyang Jin, Dianning He, Shuaishuai Hu, Lanbo Wang, Han Li, Shouliang Qi

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Objective: Refractory epilepsy is a complicated type of epilepsy that can be difficult to diagnose. Recent technological advancements have made resting-state functional magnetic resonance (rsfMRI) a vital technique for studying brain activity. However, there is still much to learn about rsfMRI. Investigating rsfMRI connectivity may aid in the detection of abnormal activities. In this paper, we propose studying the functional connectivity of rsfMRI candidates to diagnose epilepsy. Methods: 45 rsfMRI candidates, comprising 26 with refractory epilepsy and 19 healthy controls, were enrolled in this study. A data-driven approach known as independent component analysis (ICA) was used to achieve our goal. First, rsfMRI data from both patients and healthy controls were analyzed using group ICA. The components that were obtained were then spatially sorted to find and select meaningful ones. A two-sample t-test was also used to identify abnormal networks in patients and healthy controls. Finally, based on the fractional amplitude of low-frequency fluctuations (fALFF), a chi-square statistic test was used to distinguish the network properties of the patient and healthy control groups. Results: The two-sample t-test analysis yielded abnormal in the default mode network, including the left superior temporal lobe and the left supramarginal. The right precuneus was found to be abnormal in the dorsal attention network. In addition, the frontal cortex showed an abnormal cluster in the medial temporal gyrus. In contrast, the temporal cortex showed an abnormal cluster in the right middle temporal gyrus and the right fronto-operculum gyrus. Finally, the chi-square statistic test was significant, producing a p-value of 0.001 for the analysis. Conclusion: This study offers evidence that investigating rsfMRI connectivity provides an excellent diagnosis option for refractory epilepsy.

Keywords: ICA, RSN, refractory epilepsy, rsfMRI

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Authors: Natalia Espinosa, Arthur Amorim, Rudolf Huebner

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Epilepsy is a chronic neural disease and around 50 million people in the world suffer from this disease, however, in many cases, the individual acquires resistance to the medication, which is known as drug-resistant epilepsy, where a detection system is necessary. This paper showed the development of an automatic system for seizure detection based on artificial neural networks (ANN), which are common techniques of machine learning. Discrete Wavelet Transform (DWT) is used for decomposing electroencephalogram (EEG) signal into main brain waves, with these frequency bands is extracted features for training a feedforward neural network with backpropagation, finally made a pattern classification, seizure or non-seizure. Obtaining 95% accuracy in epileptic EEG and 100% in normal EEG.

Keywords: Artificial Neural Network (ANN), Discrete Wavelet Transform (DWT), Epilepsy Detection , Seizure.

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Authors: K. E. Ch. Vidyasagar, M. Moghavvemi, T. S. S. T. Prabhat

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Epilepsy is a global problem, and with seizures eluding even the smartest of diagnoses a requirement for automatic detection of the same using electroencephalogram (EEG) would have a huge impact in diagnosis of the disorder. Among a multitude of methods for automatic epilepsy detection, one should find the best method out, based on accuracy, for classification. This paper reasons out, and rationalizes, the best methods for classification. Accuracy is based on the classifier, and thus this paper discusses classifiers like quadratic discriminant analysis (QDA), classification and regression tree (CART), support vector machine (SVM), naive Bayes classifier (NBC), linear discriminant analysis (LDA), K-nearest neighbor (KNN) and artificial neural networks (ANN). Results show that ANN is the most accurate of all the above stated classifiers with 97.7% accuracy, 97.25% specificity and 98.28% sensitivity in its merit. This is followed closely by SVM with 1% variation in result. These results would certainly help researchers choose the best classifier for detection of epilepsy.

Keywords: classification, seizure, KNN, SVM, LDA, ANN, epilepsy

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Authors: Murali Munisamy, Gauthaman Karunakaran, Mubarak Al-Gahtany, Vivekanandhan Subbiah, M. Manjari Tripati

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Background: Sodium valproate is a widely prescribed broad-spectrum anti-epileptic drug. It shows high inter-individual variability in pharmacokinetics and pharmacodynamics and has a narrow therapeutic range. We evaluated the effects of polymorphic uridine diphosphate glucuronosyltransferase (UGT1A9) metabolizing enzyme on the pharmacokinetics of sodium valproate in the patients with epilepsy who showed toxicity to therapy. Methods: Genotype analysis of the patients was made with polymerase chain–restriction fragment length polymorphism (RFLP) with sequencing. Plasma drug concentrations were measured with reversed phase high-performance liquid chromatography (HPLC) and concentration–time data were analyzed by using a non-compartmental approach. Results: The results of this study suggested a significant genotypic as well as allelic association with valproic acid toxicity for UGT1A9 polymorphic enzymes. The elimination half-life (t 1/2=40.2 h) of valproic acid was longer and the clearance rate (CL=937 ml/h) was lower in the poor metabolizers group of UGT1A9 polymorphism who showed toxicity than in the intermediate metabolizers group (t1/2=35.5 h, CL=1042 ml/h) or the extensive metabolizers group (t1/2=26. h, CL=1,302 ml/h). Conclusion: Our findings suggest that the UGT1A9 genetic polymorphism plays a significant role in the steady state concentration of sodium valproate, and it thereby has an impact on the toxicity of the sodium valproate used in the patients with epilepsy.

Keywords: UGT1A9, sodium valporate, pharmacogenetics, polymorphism

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Authors: Samira-Sadat JamaliDinan, Haidar Almohri, Mohammad-Reza Nazem-Zadeh

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Lateralization of temporal lobe epilepsy (TLE) is very important for positive surgical outcomes. We propose a machine learning framework to ultimately identify the epileptogenic hemisphere for temporal lobe epilepsy (TLE) cases using magnetoencephalography (MEG) coherence source imaging (CSI) and diffusion tensor imaging (DTI). Unlike most studies that use classification algorithms, we propose an effective clustering approach to distinguish between normal and TLE cases. We apply the famous Minkowski weighted K-Means (MWK-Means) technique as the clustering framework. To overcome the problem of poor initialization of K-Means, we use particle swarm optimization (PSO) to effectively select the initial centroids of clusters prior to applying MWK-Means. We demonstrate that compared to K-means and MWK-means independently, this approach is able to improve the result of a benchmark data set.

Keywords: temporal lobe epilepsy, machine learning, clustering, magnetoencephalography

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Authors: Mostafa Mohammadpour, Christoph Kapeller, Christy Li, Josef Scharinger, Christoph Guger

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Interictal spikes provide valuable information on electrocorticography (ECoG), which aids in surgical planning for patients who suffer from refractory epilepsy. However, the shape and temporal dynamics of these spikes remain unclear. The purpose of this work was to analyze the shape of interictal spikes and measure their distance to the seizure onset zone (SOZ) to use in epilepsy surgery. Thirteen patients' data from the iEEG portal were retrospectively studied. For analysis, half an hour of ECoG data was used from each patient, with the data being truncated before the onset of a seizure. Spikes were first detected and grouped in a sequence, then clustered into interictal epileptiform discharges (IEDs) and non-IED groups using two-step clustering. The distance of the spikes from IED and non-IED groups to SOZ was quantified and compared using the Wilcoxon rank-sum test. Spikes in the IED group tended to be in SOZ or close to it, while spikes in the non-IED group were in distance of SOZ or non-SOZ area. At the group level, the distribution for sharp wave, positive baseline shift, slow wave, and slow wave to sharp wave ratio was significantly different for IED and non-IED groups. The distance of the IED cluster was 10.00mm and significantly closer to the SOZ than the 17.65mm for non-IEDs. These findings provide insights into the shape and spatiotemporal dynamics of spikes that could influence the network mechanisms underlying refractory epilepsy.

Keywords: spike propagation, spike pattern, clustering, SOZ

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Authors: Bliss Singhal

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Epilepsy is a prevalent neurological disorder affecting approximately 50 million individuals worldwide and 1.2 million Americans. There exist millions of pediatric patients with intractable epilepsy, a condition in which seizures fail to come under control. The occurrence of seizures can result in physical injury, disorientation, unconsciousness, and additional symptoms that could impede children's ability to participate in everyday tasks. Predicting seizures can help parents and healthcare providers take precautions, prevent risky situations, and mentally prepare children to minimize anxiety and nervousness associated with the uncertainty of a seizure. This research proposes a comprehensive framework to predict seizures in pediatric patients by evaluating machine learning algorithms on unimodal neuroimaging data consisting of electroencephalogram signals. The bandpass filtering and independent component analysis proved to be effective in reducing the noise and artifacts from the dataset. Various machine learning algorithms’ performance is evaluated on important metrics such as accuracy, precision, specificity, sensitivity, F1 score and MCC. The results show that the deep learning algorithms are more successful in predicting seizures than logistic Regression, and k nearest neighbors. The recurrent neural network (RNN) gave the highest precision and F1 Score, long short-term memory (LSTM) outperformed RNN in accuracy and convolutional neural network (CNN) resulted in the highest Specificity. This research has significant implications for healthcare providers in proactively managing seizure occurrence in pediatric patients, potentially transforming clinical practices, and improving pediatric care.

Keywords: intractable epilepsy, seizure, deep learning, prediction, electroencephalogram channels

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Authors: Mohmmad A. Obeidat, Amjed Al Fahoum, Ayman M. Mansour

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The recognition of an abnormal activity of the brain functionality is a vital issue. To determine the type of the abnormal activity either a brain image or brain signal are usually considered. Imaging localizes the defect within the brain area and relates this area with somebody functionalities. However, some functions may be disturbed without affecting the brain as in epilepsy. In this case, imaging may not provide the symptoms of the problem. A cheaper yet efficient approach that can be utilized to detect abnormal activity is the measurement and analysis of the electroencephalogram (EEG) signals. The main goal of this work is to come up with a new method to facilitate the classification of the abnormal and disorder activities within the brain directly using EEG signal processing, which makes it possible to be applied in an on-line monitoring system.

Keywords: EEG, wavelet, epilepsy, detection

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Authors: Maurice Ntahobari, Levin Kuhlmann, Mario Boley, Zhinoos Razavi Hesabi

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For machine learning based epileptic seizure prediction, it is important for the model to be implemented in small implantable or wearable devices that can be used to monitor epilepsy patients; however, current state-of-the-art methods are complex and computationally intensive. We use Shapley Additive Explanation (SHAP) to find relevant intracranial electroencephalogram (iEEG) features and improve the computational efficiency of a state-of-the-art seizure prediction method based on the extra trees classifier while maintaining prediction performance. Results for a small contest dataset and a much larger dataset with continuous recordings of up to 3 years per patient from 15 patients yield better than chance prediction performance (p < 0.004). Moreover, while the performance of the SHAP-based model is comparable to that of the benchmark, the overall training and prediction time of the model has been reduced by a factor of 1.83. It can also be noted that the feature called zero crossing value is the best EEG feature for seizure prediction. These results suggest state-of-the-art seizure prediction performance can be achieved using efficient methods based on optimal feature selection.

Keywords: machine learning, seizure prediction, extra tree classifier, SHAP, epilepsy

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Authors: Muhammad Bilal, Amelia Jane Llyod, Habsah Mohamad, Jia Hui Wong, Abdul Aziz Mohamed Yusoff, Jafri Malin Abdullah, Jingli Zhang

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Epilepsy is one of the major brain afflictions occurs with uncontrolled excitation of cortex; disturbed 50 million of world’s population. About 25 percent of patients subjected to adverse effects from antiepileptic drugs (AEDs) such as depression, nausea, tremors, gastrointestinal symptoms, osteoporosis, dizziness, weight change, drowsiness, fatigue are commonly observed indications; therefore, new drugs are required to cure epilepsy. GABA is principle inhibitory neurotransmitter, control excitation of the brain. Mutation or dysfunction of GABA receptor is one of the primary causes of epilepsy, which is confirmed from many acquired models of epilepsy like traumatic brain injury, kindling, and status epilepticus models of epilepsy. GABA receptor has 3 distinct types such as GABA (A), GABA (B), GABA(C).GABA (A) receptor has 20 different subunits, α1β2γ2 subunits composition of GABA (A) receptor is the most used combination of subunits for screening of compounds against epilepsy. We expressed α1β2γ2s subunits of GABA (A) Receptor in Xenopus leavis oocytes and examined the enhancement potential of 4-Hydroxybenzoic acid compound on GABA (A) receptor via two-electrode voltage clamp current recording technique. Bamboo shoots are the young, tender offspring of bamboo, which are usually harvested after a cultivating period of 2 weeks. Proteins, acids, fat, starch, carbohydrate, fatty acid, vitamin, dietary fiber, and minerals are the major constituent found systematically in bamboo shoots. These shoots reported to have anticancer, antiviral, antibacterial activity, also possess antioxidant properties due to the presence of phenolic compounds. Student t-test analysis suggested that 4- hydroxybenzoic acid positively allosteric GABA (A) receptor, increased normalized current amplitude to 1.0304±0.0464(p value 0.032) compared with vehicle. 4-Hydrobenzoic acid, a compound from Dendrocalamus Asper bamboo shoot gives new insights for future studies on bamboo shoots with motivation for extraction of more compounds to investigate their effects on human and rodents against epilepsy, insomnia, and anxiety.

Keywords: α1β2γ2S, antiepileptic, bamboo shoots, epilepsy GABA (A) receptor, two-microelectrode voltage clamp, xenopus laevis oocytes

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Authors: Mohammad Zavid Parvez, Manoranjan Paul

Abstract:

Epilepsy is a common neurological disorders characterized by sudden recurrent seizures. Electroencephalogram (EEG) is widely used to diagnose possible epileptic seizure. Many research works have been devoted to predict epileptic seizure by analyzing EEG signal. Seizure prediction by analyzing EEG signals are challenging task due to variations of brain signals of different patients. In this paper, we propose a new approach for feature extraction based on phase correlation in EEG signals. In phase correlation, we calculate relative change between two consecutive segments of an EEG signal and then combine the changes with neighboring signals to extract features. These features are then used to classify preictal/ictal and interictal EEG signals for seizure prediction. Experiment results show that the proposed method carries good prediction rate with greater consistence for the benchmark data set in different brain locations compared to the existing state-of-the-art methods.

Keywords: EEG, epilepsy, phase correlation, seizure

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Authors: K. Power, M. White, B. Dunleavey, E. Comerford, C. Doherty, N. Delanty, R. Corbridge, M. Fitzsimons

Abstract:

Introduction: Providing people with access to their own healthcare information and engaging them as co-authors of their health record can promote better transparency, trust, and inclusivity in the healthcare system. With the advent of electronic health records, there is a move towards involving patients as partners in their healthcare by providing them with access to their own health data via electronic patient portals (ePortal). For example, a recently developed ePortal to the Irish National Epilepsy Electronic Patient Record (EPR) provides access to summary medical records, tools for Patient Reported Outcomes (PROM), health goal-setting and preparation for clinical appointments. Aim: To determine what people with epilepsy (their families/carers) value about the Irish epilepsy ePortal. Methods: A socio-technical process was employed recruiting 30 families of people with epilepsy who also have an intellectual disability (ID). Family members who are a care partner of the person with epilepsy (PWE) were invited to co-design, develop and implement the ePortal. Family members engaged in usability and utility testing which involved a face to face meeting to learn about the ePortal, register for a user account and evaluate its structure and content. Family members were instructed to login to the portal on at least two separate occasions following the meeting and to complete a self-report evaluation tool during this time. The evaluation tool, based on a Usability Questionnaire (Lewis, 1993), consists of a short assessment of comfort using technology, instructions for using the ePortal and some tasks to complete. Tasks included validating summary record details, assessing ePortal ease of use, evaluation of information presented. Participants were asked for suggestions on how to improve the portal and make it more applicable to PWE who also have an ID. Results: Family members responded positively to the ePortal and valued the ability to share information between clinicians and care partners; use the ePortal as a passport between different healthcare settings (e.g., primary care to hospital). In the context of elderly parents of PWE, the ePortal is valued as a tool for supporting shared care between family members. Participants welcomed the facility to log lists of questions and goals to discuss with the clinician at the next clinical appointment as a means of improving quality of care. Participants also suggested further enhancements to the ePortal such as access to clinic letters which can provide an aide memoir in terms of the careplan agreed with the clinical team. For example, through the ePortal, people could see what investigations or therapies are scheduled. Conclusion: The Epilepsy Patient Portal is accessible via a range of devices such as smartphones and tablets. ePortals have the potential to help personalise care, improve patient involvement in clinical decision making, engage them as quality and safety partners, and help clinicians be more responsive to patient needs. Acknowledgement: The epilepsy ePortal project is part of PISCES, a Lighthouse Project funded by eHealth Ireland and HSE to help build an understanding of the benefits of eHealth technologies in the Irish Healthcare System.

Keywords: electronic patient portal, electronic patient record, epilepsy, intellectual disability, usability testing

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Authors: Željka Rogač, Dejan Stevanović, Sara Bečanović, Ljubica Božić, Aleksandar Dimitrijević, Dragana Bogićević, Dimitrije Nikolić

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Children with epilepsy face changes in cognitive functioning, the appearance of symptoms of psychopathology and a decline in their quality of life. Factors related to epileptic seizures and the side effects of AEDs are considered to be potential causes of these changes.These changes can be prevented by prompt action, replacement of AEDs, psychological and psychiatric treatment, and social support. However, a review of literature has not yielded a conclusion as to when it is best to react, i.e., when changes in the functioning of children with newly-diagnosed epilepsy appears. The primary goal of this study was to investigate the impact of the most commonly used AEDs on cognitive status, behavior, anxiety and depression, as well as quality of life of children with newly-diagnosed epilepsy, during the first six months of treatment. This is a non-interventional, prospective study involving six-month monitoring of cognitive status, internalizing and externalizing symptoms, as well as quality of life of children with newly-diagnosed epilepsy, and the impact of antiepileptic drugs on these domains. Children with new-onset epilepsy and their parents, immediately after the introduction of antiepileptic drugs as well as six months later, filled out appropriate questionnaires (RCADS, NCBRF, CHEQOL-25, KIDSCREEN-10, AEP). At the same time, a psychologist performed the psychological testing of the child (REVISK). At the very beginning of REVISK treatment, a reduced VIQ was established, while after six months there was a significant decrease in IQ, VIQ and especially PIQ, under the influence of primary cognitive potentials and the development of depressive symptoms. All scores of the RCADS and NCBFR questionnaires were significantly elevated after six months while internalizing and externalizing symptoms affected each other. The development of depressive symptoms was significantly influenced by AED. The scores of the CHEQOL25 and KIDSCREEN10 questionnaires were significantly reduced, influenced by the adverse effects of AED and quality of life at the start of treatment. Side effects of AEDs, were significantly associated with depressive symptoms and reduced quality of life and did not significantly affect cognitive decline, anxiety, ADHD, and behavioral disorders during the first six months.

Keywords: epilepsy, children, AEDs, cognition, behavior, ADHD, anxiety, depression, QOL

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Authors: Seolah Lee, Yoohyk Jang, Soyoung Lee, Kon Chu, Sang Kun Lee

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Objective: The International League Against Epilepsy (ILAE) currently suggests a topiramate serum level range of 5-20 mg/L. However, numerous institutions have observed substantial drug response at lower levels. This study aims to investigate the correlation between topiramate serum levels, drug responsiveness, and adverse events to establish a more accurate and tailored therapeutic range. Methods: We retrospectively analyzed topiramate serum samples collected between January 2017 and January 2022 at Seoul National University Hospital. Clinical data, including serum levels, antiseizure regimens, seizure frequency, and adverse events, were collected. Patient responses were categorized as "insufficient" (reduction in seizure frequency <50%) or "sufficient" (reduction ≥ 50%). Within the "sufficient" group, further subdivisions included seizure-free and tolerable seizure subgroups. A population pharmacokinetic model estimated serum levels from spot measurements. ROC curve analysis determined the optimal serum level cut-off. Results: A total of 389 epilepsy patients, with 555 samples, were reviewed, having a mean dose of 178.4±117.9 mg/day and a serum level of 3.9±2.8 mg/L. Out of the samples, only 5.6% (n=31) exhibited insufficient response, with a mean serum level of 3.6±2.5 mg/L. In contrast, 94.4% (n=524) of samples demonstrated sufficient response, with a mean serum level of 4.0±2.8 mg/L. This difference was not statistically significant (p = 0.45). Among the 78 reported adverse events, logistic regression analysis identified a significant association between ataxia and serum concentration (p = 0.04), with an optimal cut-off value of 6.5 mg/L. In the subgroup of patients receiving monotherapy, those in the tolerable seizure group exhibited a significantly higher serum level compared to the seizure-free group (4.8±2.0 mg/L vs 3.4±2.3 mg/L, p < 0.01). Notably, patients in the tolerable seizure group displayed a higher likelihood of progressing into drug-resistant epilepsy during follow-up visits compared to the seizure-free group. Significance: This study proposed an optimal therapeutic concentration for topiramate based on the patient's responsiveness to the drug and the incidence of adverse effects. We employed a population pharmacokinetic model and analyzed topiramate serum levels to recommend a serum level below 6.5 mg/L to mitigate the risk of ataxia-related side effects. Our findings also indicated that topiramate dose elevation is unnecessary for suboptimal responders, as the drug's effectiveness plateaus at minimal doses.

Keywords: topiramate, therapeutic range, low dos, antiseizure effect

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Authors: Atitallah Sofien, Bouyahia Olfa, Krifi Farah, Missaoui Nada, Ben Rabeh Rania, Yahyaoui Salem, Mazigh Sonia, Boukthir Samir

Abstract:

Introduction: Cutis marmorata telangiectatica congenita (CMTC) is a rare cutaneous vascular malformation. It most often appears at birth or during the first days of life. Its origin is still unknown. It associates a livedo with telangiectasias of diffuse or segmental topography. In rare cases, it can be associated with neurological disorders such as macrocephaly and, less frequently, with epilepsy. Methodology: We report a case of an infant with Macrocephaly- Cutis marmorata telangiectatica congenita syndrome associated with epilepsy. Results: This is the case of a one month and 15 days old female infant from a non-consanguineous marriage, admitted for a status epilepticus in the context of apyrexia. Infectious and metabolic causes had been eliminated. Physical examination had shown non-infiltrated and reticular livedoid erythematous patches affecting the left upper limb and atrophic on the back of the left hand. Cerebral magnetic resonance imaging (MRI) showed thin layers of bifrontal, temporal, and left parietal hygromas associated with the widening of the bifrontal subarachnoid spaces. The electroencephalogram showed a well-organized sleep tracing with a single right occipital paroxysmal abnormality. Antiepileptic treatment has been administered with good clinical evolution and regression of the skin lesion and a control electroencephalogram without abnormality. Conclusion: This observation illustrates an association of CMTC with both macrocephaly and epilepsy. This pathology, which is relatively benign and has a good prognosis, generally does not require treatment. However, a detailed examination must be carried out, and a follow-up plan must be put in place for each patient presenting with CMTC, given the risk of association with other abnormalities, which can be potentially serious.

Keywords: cutis marmorata telangiectatica congenita, macrocephaly, epilepsy, children

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Authors: Leila Maleki, Ahmad Esmali Kooraneh, Hossein Taghi Derakhshi

Abstract:

Neural activity in the human brain starts from the early stages of prenatal development. This activity or signals generated by the brain are electrical in nature and represent not only the brain function but also the status of the whole body. At the present moment, three methods can record functional and physiological changes within the brain with high temporal resolution of neuronal interactions at the network level: the electroencephalogram (EEG), the magnet oencephalogram (MEG), and functional magnetic resonance imaging (fMRI); each of these has advantages and shortcomings. EEG recording with a large number of electrodes is now feasible in clinical practice. Multichannel EEG recorded from the scalp surface provides a very valuable but indirect information about the source distribution. However, deep electrode measurements yield more reliable information about the source locations، Intracranial recordings and scalp EEG are used with the source imaging techniques to determine the locations and strengths of the epileptic activity. As a source localization method, Low Resolution Electro-Magnetic Tomography (LORETA) is solved for the realistic geometry based on both forward methods, the Boundary Element Method (BEM) and the Finite Difference Method (FDM). In this paper, we review The findings EEG- LORETA about epilepsy.

Keywords: epilepsy, EEG, EEG-LORETA

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