• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2009.05a
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• pp.209-210
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• 2009

수면단계는 수면감성을 평가하는데 있어서 중요한 생리지표로서 사용되어왔다. 그러나 수면다원검사를 이용한 전통적 수면단계 분류방법은 뇌전도, 안전도, 심전도, 근전도 등을 종합적으로 측정하므로 수면단계를 비교적 정확히 분류할 수 있지만 피험자에게 심한 구속감을 주는 문제가 있다. 본 연구에서는, 각성상태에서 교감신경계가 지배적인 반면에 수면 중에는 부교감신경계가 더 활동적인 점에 착안하여 수면단계를 간단히 분류할 수 있는 방법을 찾고자 수면단계에 따른 심박동변이도(heart rate variability: HRV)를 분석하였다. 단일채널 심전도를 이용하여 수면단계별로 HRV 의 교감신경계/부교감신경계 활성도의 비율을 분석한 결과, W(wakefulness) 단계가 NREN(non REM) 2 단계, 3 단계, 4 단계에 비하여 높게 나타났으며, NREM 4 단계는 REM(rapid eye movement) 단계와 NREM 1단계에 비하여 낮게 나타났다. 또한 교감신경계/부교감신경계 활성도 비율의 수면단계에 따라 변화하는 양상은 W, REM, NREM 1, 2, 3, 4 단계의 순으로 단조 감소하였다.

• Sleep Medicine and Psychophysiology
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• v.10 no.1
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• pp.52-60
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• 2003

Objectives: Stage 1 sleep provides important information regarding interpretation of nocturnal polysomnography, particularly sleep onset. It is a short transition period from wakeful consciousness to sleep. The lack of prominent sleep events characterizing stage 1 sleep is a major obstacle in automatic sleep stage scoring. In this study, utilization of simultaneous EEG and EOG processing and analyses to detect stage 1 sleep automatically were attempted. Methods: Relative powers of the alpha waves and the theta waves were calculated from spectral estimation. A relative power of alpha waves less than 50% or relative power of theta waves more than 23% was regarded as stage 1 sleep. SEM(slow eye movement) was defined as the duration of both-eye movement ranging from 1.5 to 4 seconds, and was also regarded as stage 1 sleep. If one of these three criteria was met, the epoch was regarded as stage 1 sleep. Results were compared to the manual rating results done by two polysomnography experts. Results: A total of 169 epochs were analyzed. The agreement rate for stage 1 sleep between automatic detection and manual scoring was 79.3% and Cohen’s Kappa was 0.586 (p<0.01). A significant portion (32%) of automatically detected stage 1 sleep included SEM. Conclusion: Generally, digitally-scored sleep staging shows accuracy up to 70%. Considering potential difficulty in stage 1 sleep scoring, accuracy of 79.3% in this study seems to be strong enough. Simultaneous analysis of EOG differentiates this study from previous ones which mainly depended on EEG analysis. The issue of close relationship between SEM and stage 1 sleep raised by Kinnari remains a valid one in this study.

• Sleep Medicine and Psychophysiology
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• v.13 no.2
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• pp.45-51
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• 2006

Obstructive sleep apnea (OSA) syndrome disrupts normal sleep. However, there were few studies to evaluate the asymmetric distribution, the one of the important factors of normal sleep in OSA subjects. We hypothesized that asymmetry would be broken in OSA patients. 49 male subjects with the complaint of heavy snoring were studied with polysomnography. We divided them into two groups based on the apnea-hypopnea index (AHI) fifteen: 13 simple snoring group (SSN, average AHI $5.9{\pm}4.4$) and 32 OSA group (average AHI $47.3{\pm}23.9$). We compared split sleep variables between the first half and the second half of sleep within each group with paired t-test for the evaluation of asymmetry. Changes of sleep architecture of OSA were higher stage 1 sleep% (S1), total arousal index (TAI), AHI, and mean heart rate (HR) and lower stage 2 sleep% (S2), REM sleep%, and mean arterial O2 saturation (SaO2) than SSN subjects. SWS and wake time after sleep onset (WASO) were not different between two groups. In split-night analysis, OSA subjects showed higher S2, slow wave sleep% (SWS), spontaneous arousal index (SAI), and mean HR in the first half, and higher REM sleep% and mean SaO2 in the second half. Those were same pattern as in SSN subjects. Mean apnea duration and longest apnea duration were higher in the second half only in the OSA. No differences of AHI, ODI, WASO, and S1 were found between the first and the second half of sleep in both groups. TAI was higher in the first half only in the SSN. SWS and WASO seemed to be influenced sensitively by simple snoring as well as OSA. Unlike our hypothesis, asymmetric distributions of major sleep architecture variables were preserved in OSA group. Losing asymmetry of TAI might be related to pathophysiology of OSA. We need more studies that include large number of subjects in the future.

• Science of Emotion and Sensibility
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• v.6 no.4
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• pp.9-14
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• 2003

Sleep stages have been useful indicator to check a person's comfortableness in a sleep, But the traditional method of scoring sleep stages with polysomnography based on the integrated analysis of the electroencephalogram(EEG), electrooculogram(EOG), electrocardiogram(ECG), and electromyogram(EMG) is too restrictive to take a comfortable sleep for the participants, While the sympathetic nervous system is predominant during a wakefulness, the parasympathetic nervous system is more active during a sleep, Cardiovascular function is controlled by this autonomic nervous system, So, we have interpreted the heart rate variability(HRV) among sleep stages to find a simple method of classifying sleep stages, Six healthy male college students participated, and 12 night sleeps were recorded in this research, Sleep stages based on the "Standard scoring system for sleep stage" were automatically classified with polysomnograph by measuring EEG, EOG, ECG, and EMG(chin and leg) for the six participants during sleeping, To extract only the ECG signals from the polysomnograph and to interpret the HRV, a Sleep Data Acquisition/Analysis System was devised in this research, The power spectrum of HRV was divided into three ranges; low frequency(LF), medium frequency(MF), and high frequency(HF), It showed that, the LF/HF ratio of the Stage W(Wakefulness) was 325% higher than that of the Stage 2(p＜.05), 628% higher than that of the Stage 3(p＜.001), and 800% higher than that of the Stage 4(p＜.001), Moreover, this ratio of the Stage 4 was 427% lower than that of the Stage REM (rapid eye movement) (p＜.05) and 418% lower than that of the Stage l(p＜.05), respectively, It was observed that the LF/HF ratio decreased monotonously as the sleep stage changes from the Stage W, Stage REM, Stage 1, Stage 2, Stage 3, to Stage 4, While the difference of the MF/(LF+HF) ratio among sleep Stages was not significant, it was higher in the Stage REM and Stage 3 than that of in the other sleep stages in view of descriptive statistic analysis for the sample group.

• Journal of Biomedical Engineering Research
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• v.25 no.1
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• pp.11-19
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• 2004

Stage 1 sleep provides important information regarding interpretation of nocturnal polysomnography, particularly sleep onset. It is a short transition period from wakeful consciousness to sleep. Lack of prominent sleep events characterizing stage 1 sleep is a major obstacle in automatic sleep stage scoring. In this study, we attempted to utilize simultaneous EEC and EOG processing and analyses to detect stage 1 sleep automatically. Relative powers of the alpha waves and the theta waves were calculated from spectral estimation. Either the relative power of alpha waves less than 50％ or the relative power of theta waves more than 23％ was regarded as stage 1 sleep. SEM (slow eye movement) was defined as the duration of both eye movement ranging from 1.5 to 4 seconds and regarded also as stage 1 sleep. If one of these three criteria was met, the epoch was regarded as stage 1 sleep. Results f ere compared to the manual rating results done by two polysomnography experts. Total of 169 epochs was analyzed. Agreement rate for stage 1 sleep between automatic detection and manual scoring was 79.3％ and Cohen's Kappa was 0.586 (p＜0.01). A significant portion (32％) of automatically detected stage 1 sleep included SEM. Generally, digitally-scored sleep s1aging shows the accuracy up to 70％. Considering potential difficulties in stage 1 sleep scoring, the accuracy of 79.3％ in this study seems to be robust enough. Simultaneous analysis of EOG provides differential value to the present study from previous oneswhich mainly depended on EEG analysis. The issue of close relationship between SEM and stage 1 sleep raised by Kinnariet at. remains to be a valid one in this study.

• 한국방재학회:학술대회논문집
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• 2010.02a
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• pp.52.2-52.2
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• 2010

본 연구에서는 자유수면의 계산을 위해 동수압을 해석하는 수치모형을 제시하였다. 동수압과 자유수면을 고려하기 위해, 비점성 Navier-Stokes 방정식을 3단계로 나누어 해석하였다. 제1단계에서는 동수압과 자유수면을 전번 시간단계에서 계산된 값으로 대입하여 차분하였으며 이 차분식은 NGMRES(Newton-Generalized Minimal Residual) 방법을 이용해 음해적으로 해석되었다. 이때 계산된 유속장은 연속방정식을 고려하지 않았으므로 각 계산격자에서 질량보존법칙을 만족하지 않을 수도 있다. 제2단계에서 유속과 동수압 보정항으로 이루어진 식을 연속방정식에 대입하여 얻어지는 타원형 방정식인 동수압-포와송 방정식을 해석하므로 여기서 얻어지는 유속은 질량보존법칙을 만족하게 된다. 마지막 3 단계에서는 자유수면과 최종 유속을 계산하였다. 새로 개발된 수치모형을 검증하기위해 정사각형 탱크에서 수면의 자유 진동 문제에 적용한 결과 수치해는 해석해와 잘 일치하였다.

• 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.

• 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.11 no.1
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• pp.37-43
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• 2004

Objective: The purpose of this paper is to study the possible differences in clinical and polysomnographic findings, depending on the presence or absence of subjective complaints of abnormal sleep behavior, in patients with RWA on polysomnography. Method: We reviewed patient records and polysomnographic data of patients referred to the Sleep Laboratory at Seoul National University Hospital from June 1996 through October 2002. We defined the manifest RBD group (n=32) as patients having both complaints of abnormal sleep behavior and RWA on polysomnography. The latent RBD group (n=20) consisted of patients who exhibited RWA on polysomnography but did not complain of abnormal sleep behavior. The clinical characteristics and polysomnographic findings between the two groups were compared and analyzed. Results: Fifty-two subjects had RWA, as detected by polysomnography (42 males and 10 females, mean age of $55.1{\pm}19.1\;years$). Subjects in the manifest RBD group were significantly older than those in the latent RBD group ($61.59{\pm}13.5$ vs. $44.70{\pm}2.76\;years$, independent t-test, p<0.01). More subjects in the manifest RBD group exhibited abnormal REM behavior on polysomnography than did subjects in the latent RBD group (81.3 vs. 50.0%, Fisher's exact test, p<0.05). No significant differences between the groups were found in the prevalence of brain disorders and primary sleep disorders, gender proportion, and sleep architecture. Conclusion: No difference in sleep architecture was found between the manifest and the latent RBD groups. Only age and the presence of abnormal sleep behavior on polysomnography differentiated the two groups. We suggest that RWA on polysomnography without complaints of abnormal sleep behavior may be early manifestation of manifest RBD. Attention to RWA on polysomnography is necessary to help prevent full-blown RBD from developing.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.1268-1271
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• 2007

트렌치 수로에서 동수압을 고려한 자유수면 흐름을 해석하고 난류 모형의 적용성을 제시하였다. 본 연구에 사용된 지배방정식으로 비정상 상태의 비압축성 유체에 대한 연속방정식과 비점성 Navier-Stokes 방정식을 사용하였다. 난류완결문제를 해결하기 위해서 $\kappa-\varepsilon$방정식을 사용하여서 난류 와점성을 구할 수 있다. 자유수면과 동수압을 고려하기 위하여 3단계로 나누어서 해석하였다. 제 1단계에서는 운동량방정식을 연직방향에 대해 음해적으로 차분하였다. 제2단계에서는 유속과 동수압 보정항으로 이루어진 식을 연속방정식에 대입하여 타원형 방정식인 동수압-포와송 방정식을 해석하여 얻어지는 유속은 질량보존법칙을 만족하게 된다. 마지막으로 자유수면과 최종유속을 보정 및 계산하였다. 본 연구에서 제시한 수치모형을 검증하기 위해서, 트렌치 수로에서 난류의 흐름에 변화를 고려하기 위해 수치모의를 하였다. 전반적으로 수치모의에 의한 결과와 실험 자료가 일치하는 경향을 보였다.