• Smart Media Journal
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• v.10 no.2
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• pp.55-60
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• 2021

In this research, we design and perform experiment to investigate whether neuronal activity patterns elicited while solving game tasks are spontaneously reactivated in during sleep. In order to recognize human activity EEG-fMRI signals are used at the same time. Experimental results shows that reward for the success of tasks performed before sleeping have an effect on sleep brain activity. The study uncovers a neural mechanism whereby rewarded life experiences are preferentially replayed and consolidated while we sleep.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.423-424
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• 2021

In this paper, we propose a system that collects data from sensors that detect the sleeping environment and adjusts the sleeping environment optimally based on the environment to help you get a good night's sleep. The sleep environment analysis is based on the determination of the sleep stage by detection of twisting through the load cell. In addition, based on data such as temperature, humidity, and illuminance, heat devices, humidifiers, blinds, etc. are controlled to create an environment in which to have a good sleep. The sleep environment control according to the sleep state can reduce fatigue when waking up by inducing a sleep state that is easy to wake up.

• Journal of Life Science
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• v.12 no.4
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• pp.375-382
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• 2002

The purpose of this experiment was to examine influence of acute exercise on nocturnal sleep which had been disrupted by caffeine(400mg$\times$3) thought the daytime. Six healthy young males aged 21.0$\times$0.2 yr with a history of low caffeine use. Subjects completed three conditions in a within-subject. At three conditions Sleep EEG were investigated: (1) nocturnal following quiet rest, (2) nocturnal sleep following the consumption of 1200mg of caffeine (3) nocturnal sleep following cycling at 60 min of 60% V $O_{2peak}$ with 1200mg of caffeine consumption. Sleep data were calculated for REM sleep, REM latency, sleep onset latency, sleep efficiency, sleep stages, SWS. Those data were analyzed using repeated-measures ANOVA of change scores. A main effect to, drug(caffeine) indicated that caffeine elicited sleep disturbance that is, TST and sleep onset latency increase and sleep efficiency and stage 4 decrease. The effects of exercise on sleep following caffeine intake generally improve sleep that is, stage 2, 3 and SWS increase and sleep onset latency decrease. A condition effect for sleep indicated sleep improvement after exercise Therefore The data supported a restorative theory of slow-wave sleep and suggest that acute exercise may be useful in promoting sleep and reducing sleep disturbance elevated by a high dose of caffeine.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.10
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• pp.207-216
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• 2013

The aim of this study is to design a classifier for sleep stage analysis and select important feature set which shows sleep stage well based on physiological signals during sleep. Sleep has a significant effect on the quality of human life. When people undergo lack of sleep or sleep-related disease, they are likely to reduced concentration and cognitive impairment affects, etc. Therefore, there are a lot of research to analyze sleep stage. In this study, after acquisition physiological signals during sleep, we do pre-processing such as filtering for extracting features. The features are used input for the new combination algorithm using genetic algorithm(GA) and neural networks(NN). The algorithm selects features which have high weights to classify sleep stage. As the result of this study, accuracy of the algorithm is up to 90.26% with electroencephalography(EEG) signal and electrocardiography(ECG) signal, and selecting features are alpha and delta frequency band power of EEG signal and standard deviation of all normal RR intervals(SDNN) of ECG signal. We checked the selected features are well shown that they have important information to classify sleep stage as doing repeating the algorithm. This research could use for not only diagnose disease related to sleep but also make a guideline of sleep stage analysis.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.10
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• pp.4467-4474
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• 2011

The purpose of this study was to examine the factor of the quality of elderly women's sleep. The subjects in this study were 198 elderly women who were at the age of 60 and up. One instrument used to assess their quality of sleep was Buysse, et. al.(1989)'s Pittsburgh Sleep Quality Index(PSQI), and the other used to check their urinary incontinence was Jackson, et. al.(1996)'s Urination Symptom Inventory. The collected data were analyzed by the statistical package SPSS 18.0. The elderly women got $7.09{\pm}4.29$ in the quality of sleep of the PSQI, and 67.7 percent suffered from sleep disorders. Among the subfactors of the PSQI, their subjective quality of sleep was low, and the quality of sleep during, sleep latency was low as well. And their sleep disturbance was great. In contrast, the effectiveness of their habitual sleep was relatively good, and they didn't take medicine such as a sleep pill a lot, and their functional sleep disorder in the daytime wasn't great. There were differences in the quality of sleep according to their education, financial state, self-rated health status and family relations, and whether they worked out on a regular basis or not made a difference to that as well. No differences were found according to age, religion, occupation and having a coffee or not. There was a positive correlation between their quality of sleep in the PSQI and urinary incontinence(r=.51, p=.000). Out of the subfactors of the PSQI, the sleep disturbance factor was most closely linked to urinary incontinence(r=.465, p=.001). Finally, urinary incontinence and subjective health status were identified as the variables that exerted a statistically significant influence on the quality of sleep. Those variables made a 31% prediction of it.

• 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.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.5
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• pp.451-458
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• 2018

This study was conducted to determine the association between quality of sleep (QOL) and the general characteristics, the health-behavioral factors, and the occupational characteristics of firefighting officers. The survey was conducted in October 2016 with a self-administered questionnaire using the Pittsburgh Sleep Quality Index (PSQI) developed by Pittsburgh University, targeting 600 male firefighting officers working at five fire stations in D city. As a result, the QOL of the study subjects measured by the PSQI revealed 13.2% to be good, and 86.8% to be poor. In relation to general characteristics and health habits, the rate of the group who had poor quality sleep was significantly higher in those with a lower subjective health status, in the non-smoking group, and in the low sleep-quality group, compared to the good sleep-quality group. For occupational characteristics, the rate of those with poor quality sleep was significantly higher in groups such as the fire suppression group, the shift work group, the hard physical labor group, the group with a lower sense of satisfaction in their work, and in the group classed as unfit for the job but who were not considering quitting their jobs, compared to their respective counterparts. Multiple regression analysis revealed factors of influence with explanatory powers of 24.4% for subjective health status, alcohol use, subjective quality of sleep, career, job position, and physical labor. The results of this study suggest that the quality of sleep for firefighting officers is significantly related to general characteristics, health-behavioral characteristics, and occupational characteristics.

• Progress in Medical Physics
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• v.10 no.3
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• pp.141-149
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• 1999

This research analyzed the HRV signals by using wavelet transform to observe the activities of autonomous nervous system in a sleep state. This research also restructured the HRV signals from electrocardiogram and by using coefficient which was obtained through wavelet transform, analyzed the signals by frequency bandwidth. Then compared the analyzed results with existing frequency analyzing method using AR model techniques. The suggested wavelet coefficient from power spectrum component in the study shows a similar tendency with the results from FFT or AR model technique. Therefore, it can be found that power spectrum analyzing method by wavelet coefficient is a useful as a tool for analyzing autonomous nervous system activities using HRV signals. Since the suggested method able to clearly depict the progression of change in time zone, which was once impossible with the existing methods, it is presumed that it will be useful in other physiological signals.

• The Science & Technology
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• v.29 no.5 s.324
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• pp.92-93
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• 1996

1963년부터 33년간 침대보급에 앞장서 왔던 ACE가 '침대는 과학'이라는 슬로건을 내걸고 ACE침대공학연구소를 설립하여 세계적인 연구소로 육성하고 있다. 이 연구소는 침대가 인체에 주는 영향을 실험하는 '컴퓨맨시스템'과 침대에 따라 수면상태가 어떻게 다른가를 연구하는 '종합수면시스템'등 다양한 연구를 진행하면서 새로운 침대문화의 길잡이 역할을 하고 있다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.7
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• pp.4656-4667
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• 2015

The purpose of this study was to investigate relations among the quality of life, health states, physical functions, cognitive functions, and social supports of the elderly at care facilities. The subjects include the elderly that were living in care facilities in some urban areas and aged 65 or older. The data of total 260 old people were used in analysis. Collected data were analyzed with descriptive statistics, t-test, ANOVA, Pearson correlation, and stepwise multiple regression. The elderly at care facilities, who were considered as physically and cognitive vulnerable, scored higher means on quality of life when they were satisfied with the length of stay and sleeping state, had higher subjective health states, had no tooth inconvenience and forgetfulness, suffered from a lower level of depression, had better cognitive functions, and received higher social supports. Quality of life had correlations with the sleeping state(r=-.20, p<.001), subjective health state(r=-.24, p<.001)s, depression(r=-.30, p<.001), and social supports(r=.30, p<.001). Social supports(${\beta}=.30$, p<.001), depression(${\beta}=-.25$, p<.001), subjective health states(${\beta}=-.22$, p<.001), length of stay(${\beta}=-.22$, p<.001), and sleeping state(${\beta}=-.12$, p=.025) turned out to predict the quality of life of the elderly at care facilities and have close relations with it. In short, the quality of life of the elderly is related to many different factors at care facilities. The findings indicate that nursing interventions and managements for quality of life require a mental and social approach or a whole person approach with a focus on the understanding of individual senior citizens rather than on physical activities and diseases.