• Clinical and Experimental Pediatrics
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• v.52 no.9
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• pp.964-970
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• 2009

Neonatal seizures are generally not only brief and subtle but also not easily recognized and are usually untreated. In sick neonates, seizures are frequently not manifested clinically but are detected only by electroencephalography (subclinical EEG seizures). This phenomenon of electroclinical dissociation is fairly common in neonates. On the other hand, neonates frequently show clinical behaviors such as stiffening, apnea, or autonomic manifestations that mimic seizures, which is usually associated with underlying encephalopathy and non-epileptic seizures. Therefore, it might be difficult to confirm the diagnosis of neonatal seizures. Early recognition of neonatal seizures is important to minimize poor neurodevelopmental outcomes, including cognitive, behavioral, and learning disabilities, as well as the development of postnatal epilepsy. EEG is a reliable tool in the determination of neonatal seizures. Continuous EEG monitoring is essential for the identification of seizures, evaluation of treatment efficacy, and prediction of the neurodevelopmental outcome. However, there is not yet a wide consensus on the optimal "standard" lead montage for the continuous EEG monitoring.

• Journal of Biomedical Engineering Research
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• v.15 no.1
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• pp.105-110
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• 1994

In this paper, we present a method to evaluate source locations and distributed region which is specified brain activity, as indicated by locations and strengths of intracranial sources, using potential gradients of interpolation polynomials and topographic mapping of the EEG records. This method can analyze the variance of source temporally or spatially and leads to enable a quantitative evaluation of potential gradients drawing methods which is now being used in the clinic. In the result, we obtained the overall potentials distribution on the entire scalp and the information of potential source locations from the EEG records of a patient which was known to epilepsy.

• Proceedings of the IEEK Conference
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• 2000.06e
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• pp.127-130
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• 2000

Classification of Electroencephalogram(EEG) makes one of key roles in the field of clinical diagnosis, such as detection for epilepsy. Spectrum analysis using the fourier transform(FT) uses the same window to signals, so classification rate decreases for nonstationary signals such as EEG's. In this paper, wavelet power spectrum method using wavelet transform which is excellent in detection of transient components of time-varying signals is applied to the classification of three types of Event Related Potential(EP) and compared with the result by fourier transform. In the experiments, two types of photic stimulation, which are caused by eye opening/closing and artificial light, are used to collect the data to be classified. After choosing a specific range of scales, scale-averaged wavelet spectrums extracted from the wavelet power spectrum is used to find features by Back-Propagation(13P) algorithm. As a result, wavelet analysis shows superiority to fourier transform for nonstationary EEG signal classification.

• Annals of Clinical Neurophysiology
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• v.2 no.1
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• pp.27-30
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• 2000

Reflex epilepsies are distinct but not clearly understood clinical entity. Various cerebral activities induced by simple stimulation including visual, auditory, somatosensory stimulation, as well as diverse functional tasks such as reading, calculation, complex thinking are believed to be seizure-inducing factors. We experienced two patients whose seizures were readily precipitated by complex, strenuous thinking. Both patients was teen-aged boy at the onset of seizure(13, and 15 years of age each) with normal physical and mental growth. Although first seizure was precipitated by watching TV and playing puzzles in each patient, initial diagnosis was idiopathic generalized epilepsy, possibly juvenile myoclonic epilepsy( JME). For the first few years, seizures were infrequent but mostly precipitated by the tasks needs concentration such as playing computer games, decision-making, mathematics, reading, or during the examination. EEG revealed various thinking process including reading hard books, drawing complex figure, complex calculation induced epileptic discharges even if it usually needs certain period of concentration. Phenytoin, valproic acid, clonazepam, vigabatrin, and lamotrigine sometimes abated their seizures but none of these made them seizure-free. Complex reflex epilepsy induced by thinking was proposed to be a separate type of epilepsy or a variant of JME. Age, sex, stereotypic seizure-inducing factors, clinical course, and refractory epilepsies in these patients highly suggested this type of epilepsy as a variant of JME but its refractoriness and unique provocation still needs more speculation.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.2
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• pp.251-256
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• 2008

Many researchers are studying about human Brain-Computer Interface(BCI) that it based on electroencephalogram(EEG) signals of multichannel. The researches of EEG signals are used for detection of a seizure or a epilepsy and as a lie detector. The researches about an interface between Brain and Computer have been studied robots control and game of using human brain as engineering recently. Especially, a field of brain studies used EEG signals is put emphasis on EEG artifacts elimination for correct signals. In this paper, we measure EEG signals as human emotions and divide it into five frequence parts. They are calculated related the percentage of selecting range to total range. the calculating values are compared standard values by Bayesian Network. lastly, we show the human face avatar as human Emotion.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.6
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• pp.832-837
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• 2007

Many researchers are studying brain activity to using functional Magnetic Resonance Imaging (fMRI), Time Resolved Spectroscopy(TRS), Electroencephalography(EEG), and etc. They are used detection of seizures or epilepsy and deception detection in the main. In this paper, we focus on emotion recognition by recording brain waves. We specially use fMRI, TRS, and EEG for measuring brain activity Researchers are experimenting brain waves to get only a measuring apparatus or to use both fMRI and EEG. This paper is measured that we take images of fMRI and TRS about brain activity as human emotions and then we take data of EEG signals. Especially, we focus on EEG signals analysis. We analyze not only original features in brain waves but also transferred features to classify into five sections as frequency. And we eliminate low frequency from 0.2 to 4Hz for EEG artifacts elimination.

• Journal of the Semiconductor & Display Technology
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• v.19 no.2
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• pp.92-95
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• 2020

In this paper, the new architecture of seizure prediction using CNN and LSTM and DWT was presented. In the proposed architecture, EEG data was labeled into a preictal and interictal section, and DWT was adopted to the preprocessing process to apply the characteristics of the time and frequency domain of the processed EEG signal. Also, CNN was applied to extract the spatial characteristics of each electrode used for EEG measurement, and LSTM neural network was applied to verify the logical order of the preictal section. The learning of the proposed architecture utilizes the CHB-MIT Scalp EEG dataset, and the sliding window technique is applied to balance the dataset between the number of interictal sections and the number of preictal sections. As a result of the simulation of the proposed architecture, a sensitivity of 81.22% and an FPR of 0.174 were obtained.

• The Korean Journal of Physiology and Pharmacology
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• v.23 no.2
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• pp.131-139
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• 2019

Manually reviewing electroencephalograms (EEGs) is labor-intensive and demands automated seizure detection systems. To construct an efficient and robust event detector for experimental seizures from continuous EEG monitoring, we combined spectral analysis and deep neural networks. A deep neural network was trained to discriminate periodograms of 5-sec EEG segments from annotated convulsive seizures and the pre- and post-EEG segments. To use the entire EEG for training, a second network was trained with non-seizure EEGs that were misclassified as seizures by the first network. By sequentially applying the dual deep neural networks and simple pre- and post-processing, our autodetector identified all seizure events in 4,272 h of test EEG traces, with only 6 false positive events, corresponding to 100% sensitivity and 98% positive predictive value. Moreover, with pre-processing to reduce the computational burden, scanning and classifying 8,977 h of training and test EEG datasets took only 2.28 h with a personal computer. These results demonstrate that combining a basic feature extractor with dual deep neural networks and rule-based pre- and post-processing can detect convulsive seizures with great accuracy and low computational burden, highlighting the feasibility of our automated seizure detection algorithm.

• Journal of Korean Neurosurgical Society
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• v.56 no.3
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• pp.248-253
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• 2014

Objective : Cortical dysplasia (CD) is one of the common causes of epilepsy surgery. However, surgical outcome still remains poor, especially with frontal lobe epilepsy (FLE), despite the advancement of neuroimaging techniques and expansion of surgical indications. The aim of this study was to focus on surgical strategies in terms of extent of resection to improve surgical outcome in the cases of FLE with CD. Methods : A total of 11 patients of FLE were selected among 67 patients who were proven pathologically as CD, out of a total of 726 epilepsy surgery series since 1992. This study categorized surgical groups into three according to the extent of resection : 1) focal corticectomy, 2) regional corticectomy, and 3) partial functional lobectomy, based on the preoperative evaluation, in particular, ictal scalp EEG onset and/or intracranial recordings, and the lesions in high-resolution MRI. Surgical outcome was assessed following Engel's classification system. Results : Focal corticectomy was performed in 5 patients and regional corticectomy in another set of 5 patients. Only 1 patient underwent partial functional lobectomy. Types I and II CD were detected with the same frequency (45.45% each) and postoperative outcome was fully satisfactory (91%). Conclusion : The strategy of epilepsy surgery is to focus on the different characteristics of each individual, considering the extent of real resection, which is based on the focal ictal onset consistent with neuroimaging, especially in the practical point of view of neurosurgery.

• Korean Journal of Psychosomatic Medicine
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• v.19 no.2
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• pp.101-108
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• 2011

Objectives:We conduct this study to investigate the common features between Attention Deficit Hyperactivity Disorder(ADHD) and epileptic patients compared to normal control. Methods:Epileptic patients were recruited from the department of pediatic in Jesus Hospital. ADHD patients were recruited from the department of neuropsychiatry in Jesus Hospital. We excluded mental retardation or brain organic pathology. We use ADHD Diagnostic System and Korean-Child Behavior Checklist(K-CBCL) to assess features of ADHD. Electroencephalogram(EEG) of ADHD, epileptic patients and normal control were analyzed and compared. Results:Compared to normal control group, inattention, reaction time deviation were increased in both ADHD and epilepsy group. EEG abnormalities(control 13.8%, epilepsy 97.1%, ADHD 40%) in three groups were reported. Conclusion:There are common features of ADHD and epileptic patients.