• Science of Emotion and Sensibility
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• v.1 no.1
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• pp.41-58
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• 1998

얼굴표정과 내적상태의 관계 모형을 수립하기 위한 기초 자료로서 얼굴표정과 내적상태의 대응 관계를 조사하였다. 심리적으로 최소유의미거리에 있는 두 내적상태는 서로 구별되는 얼굴표정으로 대응된다는 것을 확인함으로써 얼굴표정과 내적상태의 일대일 대응 관계가 성립한다는 것을 발견하였다. 얼굴표정 차원값과 내적상태 차원값의 관계 구조를 파악하기 위하여 중다희귀분석 및 정준상관분석을 실시한 결과, 쾌-불쾌는 입의 너비에 의해서 각성-수면은 눈과 입이 열린 정도에 의해서 얼굴표정에 민감하게 반영되는 것으로 나타났다. 얼굴표정 차원 열 두 개가 내적상태 차원상의 변화를 설명하는 정도는 50%내외였다. 선형모형이 이처럼 높은 예측력을 갖는다는 것은 이 두 변수 사이에 비교적 단순한 수리적 대응 구조가 존재한다는 것을 암시한다.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 1998.04a
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• pp.118-123
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• 1998

얼굴표정과 내적상태의 관계 모형을 수립하기 위한 기초 자료로서 얼굴표정과 내적상태의 대응관계를 조사하였다. 심리적으로 최소유의미거리에 있는 두 내적상태는 서로 구별되는 얼굴표정과 내적상태의 일대일 대응 관계가 성립한다는 것을 발결하였다. 얼굴표정 차원값과 내적상태 차원값의 관계 구조를 파악하기 위하여 중다회귀분석을 실시한 결과, 쾌-불쾌상태는 입의 너비에 의해서, 각성-수면상태는 눈과 입이 열린 정도에 의해서 얼굴표정에 민감하게 반영되는 것으로 나타났다. 얼굴표정 차원 열 두개가 내적상태 차원 상의 변화를 설명하는 정도는 40%내외였다. 선형모형이 이처럼 높은 예측력을 갖는다는 것은 이 두 변수 사이에 비교적 단순한 수리적 대응 구조가 존재한다는 것을 암시한다.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.819-820
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• 2006

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. To extract features, first we computed 6 indices to find out the information of 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 recordings of twenty patients with sleep apnea, snoring and excessive daytime sleepiness. We have shown that proposed method was effective for detecting the arousal events.

• Sleep Medicine and Psychophysiology
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• v.22 no.1
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• pp.11-19
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• 2015

Objectives: Whether daytime sleepiness is proportional to the severity of sleep apnea in obstructive sleep apnea syndrome (OSAS) is controversial. In this study we investigated how insomnia severity affects the association between daytime sleepiness and sleep apnea severity in OSAS. Methods: The present study included 235 male subjects who were diagnosed with OSAS based on clinical history and nocturnal polysomnography. Pearson's correlation analysis was conducted among sleep and mood-related self-reported data, polysomnographic data and demographic data of all subjects. Based on Pittsburgh Sleep Quality Index (PSQI), the subjects were divided into 2 groups; group A (n = 75; $PSQI{\leq}5$) and group B (n = 160; PSQI > 5). Partial correlation analysis was performed between the Epworth Sleepiness Scale (ESS) and other data in both groups. Multiple linear regression analysis was conducted to investigate the factors which affected the ESS in group A. Results: Pearson's correlation analysis showed weak or non-existent correlations between ESS and apnea severity data such as apnea-hypopnea index (AHI) (r = 0.148, p = 0.023), apnea index (AI) (r = 0.137, p = 0.036), hypopnea index (HI) (r = 0.058, p = 0.377), oxygen desaturation index (ODI) (r = 0.149, p = 0.022) and arousal total index (ATI) (r = 0.129, p = 0.048). Positive correlations between ESS and apnea severity data such as AHI ($r_p=0.313$, p = 0.008), AI ($r_p=0.339$, p = 0.004), ODI ($r_p=0.289$, p = 0.015) and ATI ($r_p=0.256$, p = 0.031) were observed only in group A. Multiple regression analysis showed that AI (t = 2.996, p = 0.004) and BAI (t = 2.721, p = 0.008) were associated with ESS in group A. Conclusion: The correlation between daytime sleepiness and sleep apnea severity was shown only in group A. This result suggests that associations between daytime sleepiness in OSAS and sleep apnea severity will become prominent when controlling for insomnia-related variables.

• Sleep Medicine and Psychophysiology
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• v.6 no.1
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• pp.76-84
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• 1999

Objectives: A number of studies have shown that sleep deprivation results in reduced vigilance and increased negative affects such as tension, depression and anger. However there are few studies about effects of sleep deprivation on anxiety. The purpose of this study was to investigate the effects of 40 hour sleep deprivation on state anxiety, affects, sleepiness and fatigue. The authors also intended to study the effect of trait-anxiety on these psychological variables after sleep deprivation. Methods: Twenty nine subjects(22 men, 7 women, $24.59{\pm}1.35$ years of age) participated in this study. Subjects had no past history of psychiatric disorders and physical illnesses, and had normal sleep-waking cycle without current sleep disturbances. All of the subjects completed sleep dairy for two weeks to exclude some who suffered from chronic sleep deprivation or sleep disturbances. Subjects were instructed to get a normal sleep as usual at night before the study. After awakening, subjects remained awake for 40 hours under continuous surveillance. They completed State-Trait Anxiety Inventory, Index of General Affect, Stanford Sleepiness Scale and Fatigue Questionnaire every three hours, therefore they completed the scales 14 times totally. Subjects were dictated not to take caffeine, alcohol, or any medications on the day of the study. Heavy exercises and naps were restricted too. Results: Sleep deprivation resulted in increased state anxiety, negative general affects, and increased sleepiness and fatigue(p<.001). Dividing into high trait-anxiety group and low trait-anxiety group, there was significant sleep deprivation x traitanxiety interaction effect on general affect(p<.05). But, there was no significant sleep deprivation x trait-anxiety interaction effect on state-anxiety, sleepiness and fatigue. During sleep deprivation, the highest ratings of scales on anxiety, negative affect, sleepiness and fatigue occurred between 4 : 00AM and 7 : 00AM. Conclusions: These results show that sleep deprivation results in increased anxiety, mood state disturbance and increment of sleepiness and fatigue. These findings also suggest that trait-anxiety is a factor that influences the degree of worsening in general affect caused by sleep deprivation. During sleep deprivation, the rating curves of anxiety, affect, sleepiness and fatigue show rhythmicity that may be related to circadian rhythm.

• Korean Journal of Psychosomatic Medicine
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• v.7 no.2
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• pp.241-246
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• 1999

Delirium is a syndrome characterized by impairement of consciousness, disorientation, disturbance of sleep-wake cycle, memory impairement, disturbance of perception. It is induced by many causes, which are CNS diseases(head trauma, vascular disease, brain tumor, etc), medical diseases(metabolic disorder, endocrine disturbance, cardiovascular disease) and drugs(anticholinergics, anticonvulsant, antipsychotics, cimetidine etc). Transdermal scopolamine which is usually used to prevent motion sickness has anticholinergic property, and so it can induce delirium. The authors report two cases of delirium induced by transdermal scopolamine. The cases shared common characteristics which were as follows : 1. All of two patients were elderly women. 2. Delirium symptom was abruptly occurred during trip after attaching scopolamine patches. 3. Delirium symptom was rapidly improved within 2-3 days. It is important to educate for both users and managers about directions for transdermal scopolamine patch usage to prevent delirium. And careful history taking is needed to diagnose delirium induced by transdermal scopolamine accurately.

• Sleep Medicine and Psychophysiology
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• v.19 no.2
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• pp.68-76
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• 2012

Objectives: Sleep disorders cause changes of autonomic nervous system (ANS) which affect cardiovascular system. Primary insomnia (PI) makes acceleration of sympathetic nervous system (SNS) tone by sleep deficiency and arousal. Obstructive sleep apnea syndrome (OSAS) sets off SNS by frequent arousals and hypoxemias during sleep. We aimed to compare the changes of heart rate variability (HRV) indices induced by insomnia or sleep apnea to analyze for ANS how much to be affected by PI or OSAS. Methods: Total 315 subjects carried out nocturnal polysomnography (NPSG) were categorized into 4 groups - PI, mild, moderate and severe OSAS. Severity of OSAS was determined by apnea-hypopnea index (AHI). Then we selected 110 subjects considering age, sex and valance of each group's size [Group 1 : PI (mean age=$41.50{\pm}13.16$ yrs, AHI <5, n=20), Group 2 : mild OSAS (mean age=$43.67{\pm}12.11$ yrs, AHI 5-15, n=30), Group 3 : moderate OSAS (mean age $44.93{\pm}12.38$ yrs, AHI 16-30, n=30), Group 4 : severe OSAS (mean age=$45.87{\pm}12.44$ yrs, AHI >30, n=30)]. Comparison of HRV indices among the four groups was performed with ANCOVA (adjusted for age and body mass index) and Sidak post-hoc test. Results: We found statistically significant differences in HRV indices between severe OSAS group and the other groups (PI, mild OSAS and moderate OSAS). And there were no significant differences in HRV indices among PI, mild and moderate OSAS group. In HRV indices of PI and severe OSAS group showing the most prominent difference in the group comparisons, average RR interval were $991.1{\pm}27.1$ and $875.8{\pm}22.0$ ms (p=0.016), standard deviation of NN interval (SDNN) was $85.4{\pm}6.6$ and $112.8{\pm}5.4$ ms (p=0.022), SDNN index was $57.5{\pm}5.2$ and $87.6{\pm}4.2$ (p<0.001), total power was $11,893.5{\pm}1,359.9$ and $18,097.0{\pm}1,107.2ms^2$(p=0.008), very low frequency (VLF) was $7,534.8{\pm}1,120.1$ and $11,883.8{\pm}912.0ms^2$ (p=0.035), low frequency (LF) was $2,724.2{\pm}327.8$ and $4,351.6{\pm}266.9ms^2$(p=0.003). Conclusions: VLF and LF which were correlated with SNS tone showed more increased differences between severe OSAS group and PI group than other group comparisons. We could suggest that severe OSAS group was more influential to increased SNS activity than PI group.

• Childhood Kidney Diseases
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• v.14 no.2
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• pp.210-217
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• 2010

Purpose : Diabetes mellitus (DM) is known as one of the common causes of secondary enuresis in children. However, enuresis in diabetic children is overlooked only as a symptom of polyuria due to hyperglycemia. We evaluated the prevalence of nocturnal enuresis in children with diabetes mellitus in this paper. Methods : Among children with diabetes in three hospitals in Daegu area, 117 agreed to 'Tele research by means of a questionnaire'. Results : Diabetic patients were divided into two groups: Nocturnal enuresis and nonnocturnal enuresis group. thirty-two of 117 (27.0%) patients were in enuresis group, with more daytime urination than non-nocturnal enuresis group ($4.2{\pm}1.6/3.6{\pm}1.2$ times, P=0.016). HbA1c at diagnosis was $12.0{\pm}2.3%/12.0{\pm}2.5%$, and at follow-up $9.3{\pm}2.3%/8.3{\pm}2.3%$ (P =0.042). Insulin was administered at $1.1{\pm}0.5/1.1{\pm}0.4$ units/kg/day. Ten children of enuresis (31.2%) group were monosymptomatic (MNE) and 22 (68.8%) children were non-monosymptomatic enuresis (non-MNE). Fourteen (43.8%) of enuresis group had persistent symptoms, with 5 MNE and 9 non-MNE each. HbA1c at diagnosis was $11.1{\pm}2.5$, $12.4{\pm}2.1$, higher in non-MNE (P=0.144). Average arousal during sleep was step $3.3{\pm}1.2$, $2.5{\pm}1.0$, higher in improved enuresis group (P=0.059). Conclusion : Nocturnal enuresis among DM patients is underestimated. However, considering psychological and social effects of enuresis in children, extensive and long-term studies are needed in the future to clarify relationship between prevalence and DM control.

• Sleep Medicine and Psychophysiology
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• v.27 no.2
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• pp.56-66
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• 2020

Objectives: In this study, the clinical characteristics of OSA patients and the quality of life before and after CPAP use were compared to determine the degree of improvement in quality of life according to CPAP use. Methods: Age, sex, height, weight, body mass index, Epworth Sleepiness Scale, Modified Mallampatti Score, Montreal Cognitive Assessment-Korean, and Pittsburgh Sleep Quality Index were compared between men and women through medical records. To understand the degree of improvement in quality of life resulting from use of CPAP, a personal telephone call was made to compare the VAS scores for quality of life before and after CPAP use. Results: In height (HT) (Z = -4.525, p < 0.001), weight (BW) (Z = -2.844, p < 0.05), sleep quality (PSQI) (Z = -2.671, p < 0.05), and arousal index (AI) (Z = -2.105, p < 0.05), there was a difference between men and women (p < 0.05). There was no difference in the remaining variables. Cross-analysis (Chi-square test) confirmed a difference between severity and sex of OSA. It has been found that there is no statistically significant order in size according to level-specific severity of OSA for PreCPAP QOL, PostCPAP QOL, CPAPUse Months, and CPAP4Hr/d (%) (p > 0.05). The difference between AHI before and after CPAP was 36.48 ± 21.54 (t = 11.609, p < 0.001) and the difference between QOL before and after CPAP was -25.43 ± 22.06 (t = -7.901, p < 0.001), both of which were significant (p < 0.001). Conclusion: Among OSA patients, there were differences in height (HT), weight (BW), sleep quality (PSQI), arousal index (AI), and severity of OSA between men and women, but the quality of life before and after CPAP was different. However, there was no difference between men and women in quality of life before and after CPAP. In addition, quality of life in OSA patients improved after using CPAP.

• Korean Journal of Cognitive Science
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• v.12 no.1_2
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• pp.77-89
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• 2001

Applying perceptual hierarchy of facial feature points, a neural network model for recognizing facial expressions was designed. Input data were convolution values of 150 facial expression pictures by Gabor-filters of 5 different sizes and 8 different orientations for each of 39 mesh points defined by MPEG-4 SNHC (Synthetic/Natural Hybrid Coding). A set of multiple regression analyses was performed with the rating value of the affective states for each facial expression and the Gabor-filtered values of 39 feature points. The results show that the pleasure-displeasure dimension of affective states is mainly related to the feature points around the mouth and the eyebrows, while a arousal-sleep dimension is closely related to the feature points around eyes. For the filter sizes. the affective states were found to be mostly related to the low spatial frequency. and for the filter orientations. the oblique orientations. An optimized neural network model was designed on the basis of these results by reducing original 1560(39x5x8) input elements to 400(25x2x8) The optimized model could predict human affective rating values. up to the correlation value of 0.886 for the pleasure-displeasure, and 0.631 for the arousal-sleep. Mapping the results of the optimized model to the six basic emotional categories (happy, sad, fear, angry, surprised, disgusted) fit 74% of human responses. Results of this study imply that, using human principles of recognizing facial expressions, a system for recognizing facial expressions can be optimized even with a a relatively little amount of information.