• Korean Journal of Clinical Laboratory Science
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• v.49 no.3
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• pp.294-299
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• 2017

The aim of this study was to detect the status of epilepticus and seizure based on the initial patterns observed in the first 30 minutes of continuous electroencephalogram (cEEG) monitoring. An cEEG was recorded digitally using electrodes applied according to the International 10~20 System. The EEG data were reviewed from January 2014 to December 2015. The baselines of the EEG patterns were characterized by lateralized periodic discharges, generalized periodic discharges, burst suppression, focal epileptiform, asymmetric background, generalized slowing, and generalized periodic discharges with a triphagic wave. The etiology was classified into five categories. The subjects of this study were 128 patients (age: $56.9{\pm}17.5years$, male:female, 74:54). The mean cEEG monitoring duration was $5.5{\pm}5.1$ (min:max, 1:33) days. The EEG pattern categories included lateralized periodic discharges (N=7), generalized periodic discharges (N=10), burst suppression (N=6), focal epileptiform (N=19), asymmetric background (N=24), generalized slowing (N=51), and generalized periodic discharges with a triphagic wave (N=11). The etiological classifications of the patients with status epilepticus were remote symptomatic (N=4), remote symptomatic with acute precipitant (N=9), acute symptomatic (N=6), progressive encephalopathy (N=2), and febrile seizure (N=1). cEEG monitoring was found to be useful for the diagnosis of non-convulsive epileptic seizures or status epilepticus. The seizure was confirmed by the EEG pattern.

• Clinical and Experimental Pediatrics
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• v.59 no.11
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• pp.440-445
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• 2016

Purpose: This study aimed to identify prognostic factors of neurological outcomes, including developmental delay, cerebral palsy and epilepsy in late-preterm and term infants with perinatal asphyxia. Methods: All late-preterm and term infants with perinatal asphyxia or hypoxic-ischemic insults who admitted the neonatal intensive care unit of Inje University Sanggye Paik Hospital between 2006 and 2014 and were followed up for at least 2 years were included in this retrospective study. Abnormal neurological outcomes were defined as cerebral palsy, developmental delay and epilepsy. Results: Of the 114 infants with perinatal asphyxia, 31 were lost to follow-up. Of the remaining 83 infants, 10 died, 56 had normal outcomes, and 17 had abnormal outcomes: 14 epilepsy (82.4%), 13 cerebral palsy (76.5%), 16 developmental delay (94.1%). Abnormal outcomes were significantly more frequent in infants with later onset seizure, clinical seizure, poor electroencephalography (EEG) background activity, lower Apgar score at 1 and 5 minutes and abnormal brain imaging (P<0.05). Infants with and without epilepsy showed significant differences in EEG background activity, clinical and electrographic seizures on EEG, Apgar score at 5 minutes and brain imaging findings. Conclusion: We should apply with long-term video EEG or amplitude integrated EEG in order to detect and management subtle clinical or electrographic seizures in neonates with perinatal asphyxia. Also, long-term, prospective studies with large number of patients are needed to evaluate more exact prognostic factors in neonates with perinatal asphyxia.

• Clinical and Experimental Pediatrics
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• v.51 no.8
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• pp.861-867
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• 2008

Purpose : We carried out this study to determine if there is any difference in the occurrence rate of the epileptiform discharge between awake EEG and sleep EEG and if there are any factors influencing on the occurrence rate of EEG. Methods : This study included 178 epileptic children who had visited neurology clinic of the department of pediatrics, Pusan National University Hospital from July 2005 to July 2006. The medical and EEG records of these children who had had both awake EEG and sleep EEG were reviewed. We analysed the occurrence rate of the epileptiform discharge between awake EEG and sleep EEG. We investigated the related clinical factors which included sex, seizure types, underlying causes, age at first seizure, antiepileptic drug (AED) medication, age at recording, and background activity. Results : Among 178 epileptic children, 91 patients (51.1%) showed epileptiform discharge in awake or sleep states, 10 patients (11.0%) abnormal only in awake, 40 patients (44.0%) abnormal only in sleep, 41 patients (45.0%) abnormal in both awake EEG and sleep EEG. The occurrence rate of sleep EEG was 81 of 178 patients (45.5%) which was more than that of the awake EEG (28.7%) (P<0.001). The occurrence rate of sleep EEG is more than that of the awake EEG regardless of sex and underlying causes. But there is no significant difference from awake EEG and sleep EEG in finding the epileptiform discharge in the patient with generalized seizure, younger than 5 years old at first seizure, younger than 10 years old at recording, no antiepileptic medication, and abnormal background activity. Conclusion : The sleep EEG is thought to be more helpful in the diagnosis of childhood epilepsy.

• Annals of Clinical Neurophysiology
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• v.2 no.2
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• pp.119-124
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• 2000

Background : We studied EEG changes during pilocarpine-induced status epilepticus(SE), a widely used model whose EEG characteristics have not been fully described previously. Methods : Male Sprague-Dawley rats weighing 250-350 grams were used as subjects. SE was induced 5-7 days after placement of chronic epidural electrodes, using 360-380 mg/Kg pilocarpine IP. Rats were observed with continuous EEG recording following pilocarpine injection until end of the SE episode. Results : SE occurred in 10/12 rats studied. SE began with a series of discrete seizures $11.1{\pm}3.93$ minutes after pilocarpine injection. $5.2{\pm}2.71$ seizures occurred over $10.9{\pm}4.62$ minutes, until the EEG converted to a waxing and waning pattern, during which the amplitude and frequency of epileptiform activity increased. After $1.4{\pm}1.82$ minutes, a pattern of continuous high amplitude rapid spiking was established. Continuous spiking continued for $3.4{\pm}0.48$ hours with a very gradual decline in amplitude and frequency, until periodic epileptiform discharges(PEDs) began to occur. The EEG consisted primarily of PEDs for another $7.4{\pm}3.09$ hours, until electrographic generalized seizures began to occur. These continued for $5.8{\pm}4.82$ hours until death. Duration of SE was $17.0{\pm}5.88$ hours. Flat periods were a prominent feature during all EEG patterns in this model. Conclusion : EEG features distinctive in pilocarpine SE(but not unique to it) include flat periods during all patterns and resumption of continuous spiking episodes after the onset of PEDs. The sequence of discrete seizures to waxing and waning to continuous spiking to PEDs was identical to that which has been described in humans and other animal models.

• Journal of the Ergonomics Society of Korea
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• v.32 no.1
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• pp.27-36
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• 2013

Objective: This paper describes an analysis of electroencephalography(EEG) signals to identify human errors during using digital devices in nuclear power plants(NPPs). Background: The application of an advanced main control room(MCR) has accompanied with lots of changes in different forms and features by virtue of new digital technologies. The characteristics of these digital technologies and devices provide several opportunities for the use of interface management. It can integrate into a compact single workstation in an advanced MCR, allowing workers to operate the plant with minimum physical burden under any operating condition. However these devices may introduce new types of human errors, and thus we need a means to assess and prevent such errors especially those related to digital devices. Method/Conclusion: The EEG data are relatively objective, and thus we introduce several measures to EEG analysis for obtaining the feasibility of human error identification. Application: This study may support to ensure the safety when applying digital devices in NPPs.

• Journal of Korea Multimedia Society
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• v.19 no.4
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• pp.756-764
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• 2016

In this paper, we present electroencephalography (EEG)-based power spectra analysis and auditory stimuli methods as coping mechanisms for disgust affection and phobia. Disgust affection is a negative emotion generated from trying to eliminate something harmful to one. It is usually related to mental illnesses such as obsessive-compulsive disorder, specifically phobia and depression. In our experiments, participants watched videos on horrible body mutilation and disgusting creatures, with either the original sound track or relaxing and exciting music as auditory stimulation. After watching the videos with original sound track, the participants watched the same video with a different audio background, such as soothing or cheerful music. We analyzed the EEG data utilizing relative power spectra and examined survey results of the participants. The results demonstrated that disgust affection is decreased when participants watched the video with relaxing or exciting music instead of the original soundtracks. Moreover, we confirmed that human's brainwave reacts according to types of audio and sources of disgust affection.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.493-493
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• 2000

Multi-valued decision making for transitional stochastic events was newly derived based on conditional probability of database. The two values (on-off) decision making method without transition had been proposed by one of the author in a previous work for a purpose of realizing human on-off decision making. The current method is an extension of the previous on-off decision making. By combining the conditional probability and the transitional probability, the closed form of the algorithm for the multi-valued transitional decision making was derived. The proposed multi-valued decision making was successfully applied to the determination of the five levels of the vigilance of a subject during the EEG recording; awake stage, drowsy stage and sleeping stages (stage 1, stage 2/3, REM (rapid eye movement)). The method for determining the vigilance level can be directly usable for the two purposes; selection of awake EEG segments for automatic EEG interpretation, and determination of sleep stages through sleep EEG. The proposed multi-valued decision making with a mathematical background of the probability can be applicable widely, in industries and in medical fields for purposes of the multi-valued decision making.

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

• Sleep Medicine and Psychophysiology
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• v.19 no.1
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• pp.11-17
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• 2012

Objective: Among various methods developed to quantitatively explore electroencephalograms (EEG), we focused on a wavelet method that was known to yield robust results under nonstationary conditions. The aim of this study was thus to introduce the wavelet method and demonstrate its potential use in clinical sleep studies. Method: This study involved artificial EEG specifically designed to validate the wavelet method. The method was performed to obtain time-dependent spectral power and phase angles of the signal. Synchrony of multichannel EEG was analyzed by an order parameter of the instantaneous phase. The standard methods, such as Fourier transformation and coherence, were also performed and compared with the wavelet method. The method was further validated with clinical EEG and ERP samples available as pilot studies at academic sleep centers. Result: The time-frequency plot and phase synchrony level obtained by the wavelet method clearly showed dynamic changes in the EEG waveforms artificially fabricated. When applied to clinical samples, the method successfully detected changes in spectral power across the sleep onset period and identified differences between the target and background ERP. Conclusion: Our results suggest that the wavelet method could be an alternative and/or complementary tool to the conventional Fourier method in quantifying and identifying EEG and ERP biomarkers robustly, especially when the signals were nonstationary in a short time scale (1-100 seconds).

• Journal of Biomedical Engineering Research
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• v.6 no.1
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• pp.1-4
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• 1985

Background EEG signals can be represented as the sum of a conventional AR process and an innovation process. It is know that conventional estimation techniques, such as least square estimates (LSE) or Gauasian maximum likelihood estimates (MLE-G) are optimal when the innovation process satisfies the Gaussian or presumed distribution. When the data are contaminated by outliers, however, these assumptions are not met and the power spectrum estimated by conventional estimation techniques may be fatally biased. EEG signal may be affected by artifacts, which are outliers in the statistical term. So the robust filtering estimation technique is used against those artifacts and it performs well for the contaminated EEG signal.