• Journal of Biomedical Engineering Research
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• v.24 no.6 s.81
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• pp.487-494
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• 2003

The sleep stages provides the most basic evidence for diagnosing a variety of sleep diseases. for staging sleep by analysis of EEG(electroencephalogram), it is especially important to detect the characteristic waveforms from EEG. In this paper, sleep EEG signals were analyzed using Fourier transform and continuous wavelet transform as well as discrete wavelet transform. Proposeed system methods. Fourier and wavelet for detecting of important characteristic waves(hump, sleep spindles. K-complex, hill wave, ripple wave) in sleep EEG. Sleep EEG data were analysed using Daubechies wavelet transform method and FFT method. As a result of simulation, we suggest that our neural network system attain high performance in classification of characteristic waves.

• 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.

• Sleep Medicine and Psychophysiology
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• v.14 no.1
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• pp.42-48
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• 2007

Introduction: Detrended fluctuation analysis (DFA) is used as a way of studying nonlinearity of EEG. In this study, DFA is applied on sleep EEG of normal subjects to look into its nonlinearity in terms of EEG channels and sleep stages. Method: Twelve healthy young subjects (age：$23.8{\pm}2.5$ years old, male：female=7：5) have undergone nocturnal polysomnography (nPSG). EEG from nPSG was classified in terms of its channels and sleep stages and was analyzed by DFA. Scaling exponents (SEs) yielded by DFA were compared using linear mixed model analysis. Results: Scaling exponents (SEs) of sleep EEG were distributed around 1 showing long term temporal correlation and self-similarity. SE of C3 channel was bigger than that of O1 channel. As sleep stage progressed from stage 1 to slow wave sleep, SE increased accordingly. SE of stage REM sleep did not show significant difference when compared with that of stage 1 sleep. Conclusion: SEs of Normal sleep EEG showed nonlinear characteristic with scale-free fluctuation, long-range temporal correlation, self-similarity and self-organized criticality. SE from DFA differentiated sleep stages and EEG channels. It can be a useful tool in the research with sleep EEG.

• Science of Emotion and Sensibility
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• v.15 no.4
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• pp.453-468
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• 2012

This study investigated the effects of Lavandula angustifolia (L. angustifolia) aroma on the brain electrical activity evaluated by an electroencephalogram (EEG) in the male adults since many researches were performed with females and few with males. Sleep quality of 35 male adults were analyzed by Pittsburgh sleep quality index, and they were divided into two groups of good sleep quality and poor sleep quality. EEG electrodes were attached at the frontal (F3, F4), temporal (T3, T4), occipital (O1, O2), and parietal (P3, P4) regions according to the International 10-20 system. EEG was measured for 3 min per each period of before, during, and after L. angustifolia aromatherapy. Subjects with good sleep quality showed increases in the delta power at the parietal region of both cerebral hemispheres and in the theta power at the temporal region of right hemisphere (p<0.05), and a decrease in the alpha at the parietal region of both cerebral hemispheres (p<0.05). And subjects with poor sleep quality showed increases in the delta power at the frontal region of left cerebral hemisphere and in the theta power at the frontal region of both hemispheres (p<0.05). It is concluded that L. angustifolia aroma has effects on decreasing an awakening status related-brain wave and increasing the sleep status related-brain waves in the male adults with good sleep quality, and has also effects on increasing the sleep status related-brain waves in the male adults with poor sleep quality.

• The Journal of the Korea Contents Association
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• v.21 no.2
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• pp.140-147
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• 2021

EEG(electroencephalogram) are measured to accurately determine the level of sleep in various sleep examinations. In general, measurements are more accurate as the number of sensor channels increases. EEG can interfere with sleep by attaching electrodes to the skin when measuring. It is necessary for self sleep care to select the minimum number of EEG channels that take into account both the user's discomfort and the accuracy of the measurement data. In this paper, we proposed a sleep stage analysis model based on machine learning and conducted experiments for using from one channel to four channels. We obtained estimation accuracy for sleep stage as following 82.28% for one channel, 85.77% for two channels, 80.33% for three channels and 68.87% for four channels. Although the measurement location is limited, the results of this study compare the accuracy according to the number of channels and provide information on the selection of channel numbers in the EEG sleep analysis.

• Sleep Medicine and Psychophysiology
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• v.15 no.2
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• pp.71-76
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• 2008

Introduction: In this study, we compared sleep structure, EEG characteristic of pediatric obstructive sleep apnea (OSA) and normal controls which were matched in sex and age. Methods: Fifteen children (male：female=4：11) who complained snoring and were suspected to have sleep apnea and their age and sex matched normal controls (male：female=5：10) have been done nocturnal polysomnography (NPSG). Sleep parameters, sleep apnea variables and relative spectral components of EEG from NPSG have been compared between both groups. Results: Pediatric OSA group were distinguished from normal controls in terms of apnea index, respiratory disturbance index and nadir of oxyhemoglobulin desaturation. Pediatric OSA group showed increased percent of sleep stage 1, decreased rapid eye movement sleep percent and increased delta power in O1 EEG channel. However other sleep parameters and spectral powers were not different between two groups. Conclusion: In pediatric OSA group, sleep structure parameter disruption may be not prominent as the previous studies for adult OSA group because of including mild OSA data in diagnostic criteria. In addition, EEG changes might not be distinct due to low arousal index compared to adult OSA patients. We can observe general characteristics and particularity of pediatric OSA through this study.

• Journal of the Korea Convergence Society
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• v.9 no.11
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• pp.83-90
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• 2018

Recently, many people have suffered from insomnia, labor loss, cognitive decline, and mental illness. The solution to this problem is almost entirely cognitive therapy or medication, but it is not recommended in the long term due to side effects and dependency problems. Therefore, in this paper, we propose a neuro feedback system based on portable EEG that helps induce sleeping. We design and evaluate the EEG classifier, which is the most important function to implement the system, and propose an optimized classifier modeling method for various factors that can affect performance. When using the proposed classifier, we could distinguish 97.9% of awakening and sleep phase in portable EEG.

• Journal of Life Science
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• v.22 no.2
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• pp.192-199
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• 2012

This study investigated the effects of Lavandula angustifolia (L. angustifolia) aroma on the brain electrical activity evaluated by electroencephalogram (EEG) in female adults with sleep disorders. The subjects were 28 healthy female adults and their sleep disorders were classified by the Pittsburgh Sleep Quality Index. EEG electrodes were attached at the frontal (F3, F4), temporal (T3, T4), occipital (O1, O2), parietal (P3, P4), reference, and ground regions according to the International 10-20 system. Subjects were exposed to the L. angustifolia aroma for 3 min. Results showed that L. angustifolia aroma decreased the occipital and parietal alpha powers, and increased the frontal theta power and occipital beta power in subjects with good sleep quality. On the other hand, L. angustifolia aroma increased the theta power in the all cranial regions after aroma treatment in subjects with poor sleep quality. In conclusion, L. angustifolia aroma diminishes a state of wakefulness in the brain and helps individuals to fall asleep. Therefore, L. angustifolia aroma may have beneficial effect for female adults with sleep disorders.

• Sleep Medicine and Psychophysiology
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• v.25 no.2
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• pp.82-91
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• 2018

Objectives: Synchronous electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) has been used to explore sleep stage dependent functional brain networks. Despite a growing number of sleep studies using EEG-fMRI, few studies have conducted network analysis on whole night sleep due to difficulty in data acquisition, artifacts, and sleep management within the MRI scanner. Methods: In order to perform network analysis for whole night sleep, we proposed experimental procedures and data processing techniques for EEG-fMRI. We acquired 6-7 hours of EEG-fMRI data per participant and conducted signal processing to reduce artifacts in both EEG and fMRI. We then generated a functional brain atlas with 68 brain regions using independent component analysis of sleep fMRI data. Using this functional atlas, we constructed sleep level dependent functional brain networks. Results: When we evaluated functional connectivity distribution, sleep showed significantly reduced functional connectivity for the whole brain compared to that during wakefulness. REM sleep showed statistically different connectivity patterns compared to non-REM sleep in sleep-related subcortical brain circuits. Conclusion: This study suggests the feasibility of exploring functional brain networks using sleep EEG-fMRI for whole night sleep via appropriate experimental procedures and signal processing techniques for fMRI and EEG.

• Sleep Medicine and Psychophysiology
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• v.10 no.2
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• pp.100-107
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• 2003

Objectives: Periodic leg movements in sleep (PLMS) might be subdivided based upon whether or not they are associated with visible EEG microarousals (MA). MA is considered to be responsible for nonrestorative sleep and daytime fatigue. The American Sleep Disorders Association's (ASDA) scoring rules for MA based on visual analysis of the EEG changes suggest that MA should last more than 3 seconds. However, it has been suggested that visual analysis may not detect some changes in EEG activity. This study is aimed at measuring changes in EEG spectra during PLMS without MA in order to better understand the arousing response of PLMS. Methods: Ten drug-free patients (three men and seven women) diagnosed with PLMS by polysomnography were studied. Spectral analysis of the EEG was performed in each patient on 30 episodes of PLMS without MA, chosen randomly across the night in stage 2 non-REM sleep. We applied stricter criteria for MA compared to ASDA, by defining it as a return to alpha and theta frequency lasting at least 1 second. Results: The mean PLMS index was $16.7{\pm}10.0$. The mean PLMS duration was $1.3{\pm}0.7$ seconds. Comparison of 4-second EEG activity both before and after the onset of PLMS without MA using independent t-test showed that the movements were associated with significant increase of relative activity in the delta band (p=0.000) and significant decrease of activity in the alpha (p=0.01) and sigma (p=0.000) bands. No significant decrease in the theta (p=0.05), beta (p=0.129), or gamma (p=0.062) bands was found. Conclusion: PLMS without MA was found to be associated with EEG change characterized by increase in the delta frequency band. This finding seems to be compatible with the hypothesis of an integrative hierarchy of arousal responses of Sforza's. Considering that the subjects had lower PLMS index and shorter PLMS duration than those of the previous study, it is suggested that an even less severe form of PLMS without MA could induce neurophysiologic change, which may potentially be of clinical significance.