• Journal of the Korea Convergence Society
• /
• v.7 no.2
• /
• pp.53-59
• /
• 2016

Epilepsy is a neurological disease characterized by recurrent seizures. Though the exact causes for epilepsy are unknown, genetic mutations, especially altered gene functions, have been implicated as key causative components of epilepsy. We recently identified a causing gene for the Miles-Carpenter syndrome (MCS). MCS patients have intellectual disability and epilepsy. MCS knockout (KO) zebrafish also show a seizure-like phenotype with hyperactivity of pectoral fin and jaw movement, resulting from loss of GABAergic interneurons. To evaluate MCS KO zebrafish as an epilepsy model, we tested the effects of retigabine, an anticonvulsant drug, on the movement of MCS KO zebrafish.

• Korean Journal of Clinical Laboratory Science
• /
• v.48 no.4
• /
• pp.388-393
• /
• 2016

Epilepsy, characterized by enduring the predisposition to generate epileptic seizures, was conceptually defined in 2005 as a disorder of the brain. According to the international league against epilepsy in 2014 that there is a high risk of recurrence within 10 years. The existence of interictal epileptiform discharges (IEDs) at the Electroencephalography (EEG) is an important risk factor for a possible recurrence of seizures, disproving that the seizures may increase. The purpose of this study was to analyze the correlation between recurrent seizures and epilepsy EEG findings in patients with IEDs, which was carried out to serve as the basis for the EEG to predict the prognosis of patients with epilepsy. This study included 432 adults older than 20 years of age who care for patients with epilepsy at Seoul National University Hospital, between June 2007 and December 2010. The results showed no difference between men and women in the EEG epilepsy disease, but there was a difference between various age groups. Correlation analysis showed a negative correlation between recurrence of seizures and age; it showed a positive correlation between recurrence and IEDs. In addition, age was associated with a predictive power of 10.9% and IEDs showed a predictive power of 15% on recurrent seizures. Therefore, EEG is considered as a very important test in epilepsy diagnosis. Therefore, further studies are necessary on the relationship between seizure recurrence and EEG.

• Journal of The Korean Society of Inherited Metabolic disease
• /
• v.19 no.1
• /
• pp.26-30
• /
• 2019

PCDH19-related epilepsy is an inherited disease occurring in female patients and characterized by early onset seizure, intellectual disability, and behavioral disturbances. It is caused by de novo or familial heterozygous variation of the PCDH19 gene located on Xq22.1. Our patient was hospitalized for multiple focal seizures. The magnetic resonance imaging was normal and electroencephalogram showed focal epileptiform discharges. The child's development did not progress; she began to manifest, cognitive, behavioral and language delays. Because of that, we performed an epilepsy gene panel test. We report a case of epilepsy with mental retardation limited to female patients with mutation of PCDH19.

• KIPS Transactions on Software and Data Engineering
• /
• v.10 no.12
• /
• pp.587-594
• /
• 2021

As the diagnosis using encephalography(EEG) has been expanded, various studies have been actively performed for classifying EEG automatically. This paper proposes a CNN model that can effectively classify EEG signals acquired from healthy persons and patients with epilepsy. We segment the EEG signals into sub-signals with smaller dimension to augment the EEG data that is necessary to train the CNN model. Then the sub-signals are segmented again with overlap and they are used for training the CNN model. We also propose ensemble strategy in order to improve the classification accuracy. Experimental result using public Bonn dataset shows that the CNN can detect the epileptic seizure with the accuracy above 99.0%. It also shows that the ensemble method improves the accuracy of 3-class and 5-class EEG classification.

• Korean Journal of Psychosomatic Medicine
• /
• v.28 no.1
• /
• pp.63-71
• /
• 2020

Objectives : The aim of this study was to investigate the impact of clinical and psychological factors on the parental stress of children and adolescents with epilepsy. Methods : Children and adolescents with epilepsy (n=90, age range=6-17 years) completed questionnaires on epilepsy-related variables, children's depressive symptoms (Children's Depression Inventory, CDI), children's anxiety (Revised Children's Manifest Anxiety Scale, RCMAS) and performed the scale for children's intelligence (IQ). Parents who have children and adolescents with epilepsy completed questionnaires on parental stress (Questionnaire on Resources and stress, QRS), parental anxiety (State-Trait Anxiety Inventory, STAI), children's attention problems (Abbreviated Conners Parent Rating Scale Revised, CPRS), and children's behavioral problems (Korean Child Behavior Checklist, K-CBCL). Stepwise regression analysis was performed to determine the significant predictive variables that affect parental stress. Results : In the correlational analysis, duration of seizure treatment (r=0.253, p=0.016), children's IQ (r=-0.544, p<0.001), children's attention problems (r=0.602, p<0.001), children's depressive symptoms (r=0.335, p=0.002), children's anxiety (r=0.306, p=0.004), children's behavioral problems (r=0.618, p<0.001), and parental anxiety (r=0.478, p<0.001), showed a significant correlation with parental stress. Children's behavioral problem (β=0.241, p=0.010), children's IQ (β=-0.472, p<0.001), and parental anxiety (β=0.426, p<0.001) were significantly related to the parental stress (Adjusted R²=0.619). Conclusions : Clinicians should pay attention to children's intelligence and behavioral problems and parental anxiety, which affect parental stress with children and adolescents with epilepsy.

• Journal of the Korean Child Neurology Society
• /
• v.26 no.4
• /
• pp.205-209
• /
• 2018

Purpose: Several studies have shown that magnesium plays an important role in modulating N-methyl-D-aspartate (NMDA)-related seizures by blocking NMDA ion channel receptors. Clinicians usually measure total serum magnesium levels instead of biologically active ionized magnesium levels. We compared the serum ionized magnesium ($iMg^{2+}$) level between epileptic children with and without a history of fever-triggered seizure (FTS). Methods: All epileptic children who visited the outpatient clinic or pediatric emergency department at Korea University Guro Hospital between January 2015 and July 2017 were included. Only epileptic children aged 1-8 years who were newly diagnosed within 2 years were included. Results: There were 12 children with FTS and 16 without FTS. Median serum $iMg^{2+}$ level was 0.93 (0.85-1.14, quartile) mEq/L. Serum $iMg^{2+}$ level was significantly lower in epileptic children with FTS (0.86 mEq/L) compared to those without FTS (1.10 mEq/L) (P=0.005). No difference was noted in clinical variables between the two groups. Lower serum $iMg^{2+}$ level significantly increased the risk of having FTS in epileptic children based on multivariable logistic regression analysis (odds ratio [OR]=0.028). Conclusion: Serum $iMg^{2+}$ level was significantly lower in epileptic children with FTS than in those without FTS. Measurement of biologically active serum $iMg^{2+}$ level could be considered in epileptic children with recurrent FTS. A large-scale prospective study is warranted.

• Journal of Korean Academy of Nursing
• /
• v.47 no.4
• /
• pp.499-513
• /
• 2017

Purpose: This concept analysis was done to clarify 'uncertainty in epilepsy'. Methods: Walker and Avant's methodology guided the analysis. In addition, the concept was compared with uncertainty in other health problems. Results: 'Uncertainty in epilepsy' was defined as being in the condition as seen from the epilepsy experience where cues were difficult to understand because they changed, were in discord with past ones, or they had two or more contradictory values at the same time. Uncertainty in epilepsy is evolved from appraisal of the epilepsy experience. As a result, uncertainty leads epilepsy patients, their family or health care providers to impaired functioning and proactive/passive coping behavior. Conclusion: Epilepsy patients with uncertainty need to be supported by nursing strategies for proactive, rational coping behavior. This achievement has implications for interventions aimed at changing perception of epilepsy patients, their families or health care providers who must deal with uncertainty.

• Korean Parent-Child Health Journal
• /
• v.18 no.2
• /
• pp.88-99
• /
• 2015

Purpose: This study was conducted to examine mother-child interactions and the quality of life of preschool children with epilepsy as perceived by mothers, and to investigate the relation between mother-child interactions and the quality of life of preschool children with epilepsy. Methods: Participants for this study consist of 92 mothers of children with epilepsy aged three to six years who were treated at university hospitals and a city hospital located in Seoul, Korea. The instruments used for this study were mother-child interactions of preschool children scale and the Korean version of the TAPQOL (TNO-AZL Preschool children Quality of Life). Results: The level of mother-child interactions for preschool children with epilepsy showed a mean score 125.91. The category of dyadic domain was rated the highest while the child domain category was rated the lowest. The level of mother-child interactions for preschool children with epilepsy showed a significant difference according to the mother-child relationship, birth history, seizure frequency, number of antiepileptic drugs and combined disabilities. The quality of life of children with epilepsy showed a significant difference according to the mother-child relationship, birth history, seizure frequency, number of antiepileptic drugs and combined disabilities. There is a statistically significant positive correlation between mother-child interactions and quality of life of preschool children with epilepsy. Conclusion: This study suggests that the mother-child interaction of preschool children with epilepsy showed a tendency to be led by mothers. In order to stimulate mother-child interactions, mothers should help their children enhance their reaction and participation.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.13 no.6
• /
• pp.1333-1342
• /
• 2018

Until now, Electroencephalography(: EEG) has been the most important and convenient method for the diagnosis and treatment of epilepsy. However, it is difficult to identify the wave characteristics of an epileptic EEG signals because it is very weak, non-stationary and has strong background noise. In this paper, we analyse the effect of dimensionality reduction methods on Epileptic EEG feature selection and classification. Three dimensionality reduction methods: Pincipal Component Analysis(: PCA), Kernel Principal Component Analysis(: KPCA) and Linear Discriminant Analysis(: LDA） were investigated. The performance of each method was evaluated by using Support Vector Machine SVM, Logistic Regression(: LR), K-Nearestneighbor(: K-NN), Decision Tree(: DR) and Random Forest(: RF). From the experimental result, PCA recorded 75% of highest accuracy in SVM, LR and K-NN. KPCA recorded 85% of best performance in SVM and K-KNN while LDA achieved 100% accuracy in K-NN. Thus, LDA dimensionality reduction is found to provide the best classification result for epileptic EEG signal.

• The Korean Journal of Applied Statistics
• /
• v.37 no.2
• /
• pp.139-155
• /
• 2024

This study performed a multi-classification task to classify mesial temporal lobe epilepsy with left hippocampal sclerosis patients (left mTLE), mesial temporal lobe epilepsy with right hippocampal sclerosis (right mTLE), and healthy controls (HC) using magnetoencephalography (MEG) data. We applied various artificial neural networks and compared the results. As a result of modeling with convolutional neural networks (CNN), recurrent neural networks (RNN), and graph neural networks (GNN), the average k-fold accuracy was excellent in the order of CNN-based model, GNN-based model, and RNN-based model. The wall time was excellent in the order of RNN-based model, GNN-based model, and CNN-based model. The graph neural network, which shows good figures in accuracy, performance, and time, and has excellent scalability of network data, is the most suitable model for brain research in the future.