• Sleep Medicine and Psychophysiology
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• v.3 no.2
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• pp.1-17
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• 1996

To demonstrate the clinical usefulness of electroencephalography (EEG) and factors increasing the usefulness of EEG, the authors evaluated each relationship between EEG related factors and clinical variables, and neuroimaging studies (CT and MRI)-related factors, and factors which are related with routine neurological examination for 207 patients who had been evaluated with both of EEG and neuroimaging study(CT or/and MRI). The results were as follows: 1) Abnormality of EEG findings had significant relationships with chief complaints, diagnosis, medication use, seizure attack, pathological reflex, and level of consciousness. However there were no significant correlations between abnormality of EEG findings and neuroimaging studies (CT and MRI)- related factors. 2) Laterality of EEG findings had significant relationships with abnormality, laterality, and focality of CT findings, and also with abnormality of MRI findings. But there were no significant correlations between laterality of EEG findings and clinical variables, and neurological examination-related factors. 3) Anterior-posterior distribution of EEG findings was significantly related with medication use. 4) Focality of EEG findings had significant relationships with sex, sensory dysfunction sign, and cerebellar dysfunction sign. But there were no significant correlations between focality of EEG findings and neuroimaging studies(CT and MRI) related factors. 5) Abnormal EEG pattern had significant correlations with various factors, such as age, chief complaints, duration from onset of symptom to taking MRI, seizure attack, abnormality and nature of lesion in CT findings, cortical atrophy in MRI findings, motor dysfunction sign, sensory dysfunction sign, and pathological reflex. 6) With abnormality on sleep activation, age, age of onset, seizure attack, ventricular enlargement in CT findings, and abnormality of MRI findings were significantly correlated. 7) With abnormality on hyperventilation activation, duration of illness and laterality of MRI findings were significantly correlated. Above results may suggest that abnormality of EEG findings is more closely related with functional change of the brain than structural changes of the brain and laterality of EEG findings is vice versa. And also that medication use has an influence on anterior versus posterior distribution of EEG findings and focality of EEG findings is not related with structural changes of the brain. Activation with sleep may be effective to show age differences and provocation of seizure activity and hyperventilation may be effective to detect the abnormal EEG findings by cerebrovascular insufficiency.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.5
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• pp.240-247
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• 2006

Frequent arousals during sleep degrade the quality of sleep and result in sleep fragmentation. Visual inspection of physiological signals to detect the arousal events is cumbersome and time-consuming work. The purpose of this study is to develop an automatic algorithm to detect the arousal events. The proposed method is based on time-frequency analysis and the support vector machine classifier using single channel electroencephalogram (EEG). To extract features, first we computed 6 indices to find out the informations of a subject's sleep states. Next powers of each of 4 frequency bands were computed using spectrogram of arousal region. And finally we computed variations of power of EEG frequency to detect arousals. The performance has been assessed using polysomnographic (PSG) recordings of twenty patients with sleep apnea, snoring and excessive daytime sleepiness (EDS). We could obtain sensitivity of 79.65%, specificity of 89.52% for the data sets. We have shown that proposed method was effective for detecting the arousal events.

• Korean Journal of Biological Psychiatry
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• v.19 no.1
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• pp.65-69
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• 2012

Objectives : Much is still unknown about the neurophysiological mechanisms or dynamics of the sleep onset process. Detrended fluctuation analysis (DFA) is a new tool for the analysis of electroencephalography (EEG) that may give us additional information about electrophysiological changes. The purpose of this study is to analyze long-range correlations of electroencephalographic signals by DFA and their changes in the sleep onset process. Methods : Thirty channel EEG was recorded in 61 healthy subjects (male:female=34:27, age=$27.2{\pm}3.0$ years). The scaling exponents, alpha, were calculated by DFA and compared between four kinds of 30s sleep-wakefulness states such as wakefulness, transition period, early sleep, and late sleep (stage 1). These four states were selected by the distribution of alpha and theta waves in O1 and O2 electrodes. Results : The scaling exponents, alpha, were significantly different in the four states during sleep onset periods, and also varied with the thirty leads. The interaction between the sleep states and the leads was significant. The means (${\pm}$ standard deviation) of alphas for the states were 0.94 (${\pm}0.12$), 0.98 (${\pm}0.12$), 1.10 (${\pm}0.10$), 1.07 (${\pm}0.07$) in the wakefulness, transitional period, early sleep and late sleep state respectively. The mean alpha of anterior fifteen leads was greater than that of posterior fifteen leads, and the two regions showed the different pattern of changes of the alpha during the sleep onset periods. Conclusions : The characteristic findings in the sleep onset period were the increasing pattern of scaling exponent of DFA, and the pattern was slightly but significantly different between fronto-temporal and parieto-occipital regions. It suggests that the long-range correlations of EEG have a tendency of increasing from wakefulness to early sleep, but anterior and posterior brain regions have different dynamical process. DFA, one of the nonlinear analytical methods for time series, may be a useful tool for the investigation of the sleep onset period.

• Journal of Biomedical Engineering Research
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• v.39 no.1
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• pp.55-61
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• 2018

The purpose of this study was to investigate the effect of a pillow simulated with cranial sacral therapy (CST) on sleep induction using electroencephalography (EEG). This study included 12 voluntary participants divided into experimental group (CST group) and control group (Non-CST group) to observe EEG changes. The position of the electrode for EEG measurement consists of 8 channels electrodes (Fp1, Fp2, F3, F4, T3, T4, P3 and P4). In this study, we measured the fall asleep time, change of brain activity and sleep wave ratio using EEG wave (${\delta}$, ${\theta}$, ${\alpha}$, ${\beta}$ and ${\gamma}$). As a result, the mean fall asleep time of the experimental group was shorter than that of the control group significantly (p < 0.001). Also in comparison with the control group, both the delta (d) and theta (q) wave corresponding to the slow waves showed a larger increase and the alpha (a) wave showed a larger decrease significantly. The slow waves of experimental group showed a higher rate of significant increase than the control group (p < 0.001). Therefore this study showed that pillow based on CST had an effective in improving sleep induction and quality.

• Sleep Medicine and Psychophysiology
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• v.11 no.2
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• pp.73-79
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• 2004

Digital polysomnography was developed to overcome the limitations of Rechtschaffen and Kales rule and to compensate the shortcomings of paper polysomnography. It enables easy access to and secure preservation of sleep records, and provides various displays of sleep data to enhance efficiency of visual scoring of sleep records. Rechtschaffen and Kales rule had been criticized for its ambiguity and lack of considerations in spatial information of EEG. As sleep records are acquired and processed in digital mode, they can be analyzed at microscopic and macroscopic levels. Digital analysis of sleep records provides the basis for development of new sleep measures. Sleep staging in digital polysomnography is based on the various analyses of EEG. Sleep apnea, hypopnea and periodic limb movement are detected automatically by digital analysis of respiratory signals and leg EMG. Digital polysomnography plays a complementary role to visual scoring and compensates the limitations of paper polysomnography. Digital polysomnography, including acquisition, processing and analysis of sleep records in digital mode, can be a great help in the development of sleep medicine, enabling the development of new sleep measures and the exchange of sleep records between sleep laboratories.

• Sleep Medicine and Psychophysiology
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• v.2 no.2
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• pp.133-137
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• 1995

The author reviews current knowledge about what REM sleep is and where and how it is generated. REM sleep is the state in which our most vivid dreams occur. REM sleep is identified by the simultaneous presence of a desynchronized cortical EEG, an absence of activity in the antigravity muscles(atonia), and periodic bursts of rapid eye movements. Another characteristic phenomena of REM sleep are the highly synchronized hippocampal EEG of theta frequency and the ponto-geniculo-occipital(PGO) spike. All these phenomena can be explained in terms of changes in neuronal activity. Transection studies have determined that the pons is sufficient for generating REM sleep. Lesion studies have identified a small region in the lateral pontine tegmentum corresponding to lateral portions of the nucleus reticularis pontis oralis(RPO) and the region immediately ventral to the locus coeruleus, which is required for REM sleep. Unit recording studies have found a population of cells within this region that is selectively active in REM sleep. Cholinergic neurons of the giant cell field of pontine tegmentum(ETG), which is 'REM a sleep-on cells', has shown to be critically involved in the generation of REM sleep. Noradrenergic neurons of the locus coeruleus and serotonergic neurons of the dorsal raphe, which are called 'REM sleep-off cells', appear to act in a reciprocal manner to the cholinergic neurons. It is proposed that the periodic cessations of discharge of 'REM sleep-off cells' during REM sleep might be significant for the prevention of the desensitization of receptors of these neurons.

• Journal of Ginseng Research
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• v.32 no.3
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• pp.220-225
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• 2008

The present investigation was performed to evaluate the homeostatic regulation of sleep architecture by the ethanol extract of Korea red ginseng (KRG), since the available data were often controversial. In addition, it was also interested in whether the sleep-wake stages were differently affected by low and high doses of KRG. Each adult Wistar male rat was implanted with a transmitter for recording EEG and activity via telemetry. After one week of surgery, polygraphic signs of undisturbed sleep-wake activities were recorded for 12 h (between 9:00 am and 9:00 pm) after KRG administration. KRG (10 and 100 mg/kg) increased non-rapid eye movement (NREM) sleep as well as total sleep. The total percentages of wakefulness were decreased comparably. KRG (10 mg/kg) decreased the power density of the ${\delta}-wave$ (0.75-4.5 Hz) and increased ${\alpha}-wave$ (8.0-13.0 Hz) in the NREM and rapid eye movement (REM) sleep. KRG also decreased ${\delta}-wave$ power density in wake time. However, KRG (100 mg/kg) increased ${\delta}-wave$ and decreased ${\theta}-wave$ (5.0-9.0 Hz) power density in wake time, while showed little effect on the power density in NREM and REM sleep. In conclusion, low and high doses of KRG increase spontaneous sleep and NREM sleep and differently regulate the EEG spectra in REM and NREM sleep.

• Journal of Sensor Science and Technology
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• v.13 no.3
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• pp.199-206
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• 2004

Epileptic seizures result from a temporary electrical disturbance of the brain. In this paper, a method of discriminating EEG for diagnoses of temporal lobe epilepsy is proposed. The proposed method for classification of epilepsy and sleep EEG is based on the wavelet transform and the fuzzy c-means. The magnitude and mean of wavelet coefficients for each EEG band are applied to the cluster of the FCM classifier. The proposed system show a little more accurate diagnosis for EEG by analysis of frequency for Wavelet and the success rate of 95% classification using FCM. From the simulation results by the implemented system, we demonstrated this research can be reduce doctor's labors and realize quantitative diagnosis of EEG.

• Journal of Korea Multimedia Society
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• v.23 no.2
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• pp.204-215
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• 2020

People who have sleep disorders such as insomnia take a long time to get to sleep, namely sleep latency. In order to reduce it, effective stimulations and environments to induce sleep such as ASMR or pink noise are necessary. However these have different effects and preferences for each individual. Therefore customized service and control for the sleep induction will be provide to him/her. In this paper, we proposed SIS control system which provides selectively sound control among various kinds of ASMR and pink noise according to sleep state measured from brain wave data for an individual. In order to verify the effectiveness of the system, we had conducted totally 30 experiments for 5 people, and all EEG data measured from all the people during sleep. An average of 3.7 hours was spent per experiment. In comparison experiments with and without sound control for sleep induction, the latency time was reduced by an average of 8 minutes as well as delta waves and theta waves, which appear only in deep sleep, are increased by 21%.

• 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.