• Journal of Biomedical Engineering Research
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• v.9 no.1
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• pp.101-108
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• 1988

In this paper the power spectrum of background EEG is estimated by the LMS PHD based on least mean square. At the power spectrum estimatiom, the stocastic process of background EEG is assumed to consist of the nonharmonic sinusoid and the white noise. In the LMS PHD the model parameters are obtained by the least mean square at optimal order which is obtained from the fact that the eigenvalue's fluctuation of autocorrelation matrix of the normal back-ground EEG is smaller at some order than at other order when the power spectrum of background EEG is esitmated by PHD. The optimal order of this model is the 6-th order when the eigenvalue's fluctuation of autocorrelation matrix of background EEG is considered. The estimation results are with compared the results from the Maximum Entropy Spectral Estimation and Pisarenko Harmonic Decomposition. From the comparison results. The LMS PHD is possible to estimate the power spectrum of background EEG.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.505-508
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• 1995

The automatic interpretation of awake background electroencephalogram (EEG), consisting of quantitative EEG interpretation and EEG report making, has been developed by the authors based on EEG data visually inspected by an electroencephalographer (EEGer). The present study was focused on the adaptability of the automatic EEG interpretation which was accomplished by the constructive neural network with forgetting factor. The artificial neural network (ANN) was constructed so as to give the integrative decision of the EEG by using the input signals of the intermediate judgment of 13 items of the EEG. The feature of the ANN was that it adapted to any EEGer who gave visual inspection for the training data. The developed method was evaluated based on the EEG data of 57 patients. The re-trained ANN adapted to another EEGer appropriately.

• Proceedings of the KIEE Conference
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• 1987.07b
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• pp.1271-1275
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• 1987

With the stochastic process which consists of the harmonic sinusoid and the white nosie, the power spectrum of background EEG is estimated by the Pisarenko Harmonic Decomposition. The estimating results are examined and compared with the results from the maximum entropy spectral estimation, and the optimal order of this model can be determined from the eigen value's fluctuation of autocorrelation of background EEG. From the comparing results, this paper ensures that this method is possible to analyze the power spectrum of background EEG.

• Journal of Biomedical Engineering Research
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• v.8 no.1
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• pp.69-74
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• 1987

The power spectrum of background EEG is estimated by the Plsarenko Harmonic Decomposition with the stochastic process whlch consists of the nonhamonic sinus Bid and the white nosie. The estimation results are examined and compared with the results from the maximum entropy spectral extimation, and the optimal order of this from the maximum entropy spectral extimation, and the optimal order of this model can be determined from the eigen value's fluctuation of autocorrelation of background EEG. From the comparing results, this method is possible to estimate the power spectrum of background EEG.

• Clinical and Experimental Pediatrics
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• v.54 no.1
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• pp.22-28
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• 2011

Purpose: To investigate the clinical characteristics of late-onset epilepsy combined with autism spectrum disorder (ASD), and the relationship between certain types of electroencephalography (EEG) abnormalities in ASD and associated neuropsychological problems. Methods: Thirty patients diagnosed with ASD in early childhood and later developed clinical seizures were reviewed retrospectively. First, the clinical characteristics, language and behavioral regression, and EEG findings of these late-onset epilepsy patients with ASD were investigated. The patients were then classified into 2 groups according to the severity of the EEG abnormalities in the background rhythm and paroxysmal discharges. In the severe group, EEG showed persistent asymmetry, slow and disorganized background rhythms, and continuous sharp and slow waves during slow sleep (CSWS). Results: Between the two groups, there was no statistically significant difference in mean age (P=0.259), age of epilepsy diagnosis (P=0.237), associated family history (P=0.074), and positive abnormal magnetic resonance image (MRI) findings (P=0.084). The severe EEG group tended to have more neuropsychological problems (P=0.074). The severe group statistically showed more electrographic seizures in EEG (P=0.000). Rett syndrome was correlated with more severe EEG abnormalities (P=0.002). Although formal cognitive function tests were not performed, the parents reported an improvement in neuropsychological function on the follow up checkup according to a parent's questionnaire. Conclusion: Although some ASD patients with late-onset epilepsy showed severe EEG abnormalities, including CSWS, they generally showed an improvement in EEG and clinical symptoms in the longterm follow up. In addition, severe EEG abnormalities tended to be related to the neuropsychological function.

• Proceedings of the KOSOMBE Conference
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• v.1989 no.05
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• pp.45-49
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• 1989

In this paper the feature of background EEG is extracted by LS Prony Method for the analysis of background EEG in the time domain. From the experimential results the alpha band amplitude is the largest among bands and beta band amplitude is larger than that of the delta band and theta band. The sustained time for the alpha band, the beta band, the delta band and the theta band is 2.3461(sec), 1.8980(sec), 0.3120(sec), 0.2930(sec) respectively. Consequently the alpha band and the beta band are maintained in the whole, segment. The delta band, the theta band are existed intermittently in the segment.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2001.05a
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• pp.225-230
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• 2001

Intensity of background noise is a factor significantly affecting both subjective evaluation of experienced stress level and associated electroencephalographic (EEG) responses during mental load in noisy environments. In the study on 27 subjects we analyzed the influence of the background white noise (WN) intensity on psychophysiological responses during a word recognition test. Electrocortical activity were recorded during baseline resting state and 40 s long performance on 3 similar Korean word recognition tests with different intensities of background WN (55, 70 and 85 dB).. An important finding in terms of physiological reactivity was similarity of all physiological response profiles between 55 and 70dB WN, i.e., none of physiological variables differentiated the two conditions, while 85dB WN resulted in a significantly different profile of reactions (higher fast beta power in EEG spectra). This condition was characterized by highest subjective rating of experienced stress, had more fast beta activity and had tendency of right hemisphere dominance, emphasizing the role of brain lateralization in negative affect control.

• Journal of Korean Physical Therapy Science
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• v.15 no.3
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• pp.9-17
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• 2008

Background : The conventional electroencephalography(EEG) is commonly used as aid in the diagnosis of dementia. Recently developed quantitative electroencephalography(qEEG) provides data that are not achievable by conventional EEG. The aim of this study was to find out the usefulness of quantified-EEG in dementia. Method : Twenty elderly women(10 normal elderly, 10 demented elderly) were participated in this study. EEG power and coherence was computed over 21 channels; right and left frontal, central, parietal, temporal and occipital areas. Result : The activity of ${\alpha}$ wave was more higher than others significantly at frontal and parietal areas in normal elderly, but the activity of ${\theta}$ wave was higher in demented elderly. And the activity of ${\theta}$ wave in demented elderly women was more higher than normal elderly women significantly. Conclusion : In conclusion, we discovered that quantitative EEG was used to diagnose dementia.

• Sleep Medicine and Psychophysiology
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• v.17 no.2
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• pp.75-84
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• 2010

The cyclic alternating pattern (CAP) is a periodic EEG activity in NREM sleep, characterized by sequences of transient electrocortical events that are distinct from background EEG activities. A CAP cycle consists of two periodic EEG features, phase A and subsequent phase B whose durations are 2-60 s. At least two consecutive CAP cycles are required to define a CAP sequence. The CAP phase A is a phasic EEG event, such as delta bursts, vertex sharp transients, K-complex sequences, polyphasic bursts, K-alpha, intermittent alpha, and arousals. Phase B is repetitive periods of background EEG activity. The absence of CAP more than 60 seconds or an isolated phase A is classified as non-CAP. Phase A activities can be classified into three subtypes (A1, A2, and A3), based on the amounts of high-voltage slow waves (EEG synchrony) and low-amplitude fast rhythms (EEG desynchrony). CAP rate, the percentage of CAP durations in NREM sleep is considered to be a physiologic marker of the NREM sleep instability. In insomnia, the frequent discrepancy between self-reports and polysomnographic findings could be attributed to subtle abnormalities in the sleep tracing, which are overlooked by the conventional scoring methods. The conventional scoring scheme has superiority in analysis of macrostructure of sleep but shows limited power in finding arousals and transient EEG events that are major component of microstructure of sleep. But, it has recently been found that a significant correlation exists between CAP rate and the subjective estimates of the sleep quality in insomniacs and sleep-improving treatments often reduce the amount of CAP. Thus, the extension of conventional sleep measures with the new CAP variables, which appear to be the more sensitive to sleep disturbance, may improve our knowledge on the diagnosis and management of insomnia.

• Journal of the Korean Child Neurology Society
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• v.26 no.4
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• pp.240-245
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• 2018

Purpose: Early prediction of prognosis of children resuscitated from cardiac arrest is a major challenge. We investigated the utility of electroencephalography (EEG) and laboratory studies for predicting of neurologic outcome in children resuscitated from cardiac arrest. Methods: We retrospectively analyzed medical records of patients who were resuscitated from cardiac arrest from 2006 to 2015 at the Gil Medical Center. Patients aged one month to 18 years were included. EEG analysis included background scoring, reactivity and seizure burden. EEG background was classified score 0 (normal/organized), score 1 (slow and disorganized), score 2 (discontinuous or burst suppression), and score 3 (suppressed and featureless). Neurologic outcome was evaluated by Pediatric Cerebral Performance Category (PCPC) at least 6 months after cardiac arrest. Results: Total 26 patients were evaluated. Nine patients showed good neurologic outcome (PCPC 1, 2, 3) and 17 patients showed poor neurologic outcome (PCPC 4, 5, 6). Patients of poor neurologic outcome group showed EEG background score 3 in 88.2%, whereas 44.4% in patients of good neurologic outcome group (P=0.028). Electrographic ictal discharges except non-convulsive status epilepticus were presented in 44.4% of good neurologic outcome group and 5.9% of poor neurologic outcome group (P=0.034). Ammonia and lactate levels were higher and pH levels were lower in poor outcome group than good neurologic outcome group. Conclusion: Suppressed and featureless EEG background is associated with poor neurologic outcome and electrographic seizures are associated with good neurologic outcome.