• Journal of Oral Medicine and Pain
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• v.33 no.2
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• pp.205-218
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• 2008

Interaction between pain and sleep has long been proved through many researches, and various studies are being conducted to identify its mechanism. However, these studies have targeted on patients with systemic disease, such as rheumatic disease and fibromyalgia. There are few researches on patients with orofacial pain including temporomandibular disorder(TMD). In this study, we studied interaction between pain aspect and sleep quality in 229 patients with TMD, who visited the TMJ and Orofacial pain clinic. Pittsburgh Sleep Quality Index(PSQI), Epworth sleepiness scale(ESS) questionnaire were surveyed and sleep-screening device was operated. PSQI showed that sleep quality in TMD patients with pain was poorer than that in TMD patients without pain. The ratio of poor sleeper was higher in TMD patients with pain. Especially, TMD patients with chronic pain showed obviously poorer sleep quality than TMD patients with acute pain. The result of ESS showed that patients with painful TMD showed more daytime sleepiness than painless TMD patients. The ratio of TMD patients with chronic pain who had daytime sleepiness was higher than TMD patients with acute pain, and the amount of daytime sleepiness was higher in the group of chronic pain. In TMD patients with chronic pain, only the poor sleeper(PSQI>5) presented mean ESS>10(diagnostic criteria of daytime sleepiness). There was no correlation between pain intensity and sleep quality or daytime sleepiness. The result of ApnealinkTM for screening of sleep related breathing disorder showed that only 1 patient presented AHI>5 among 19 participants. TMD patients with chronic pain presented poor sleep quality and excessive daytime sleepiness similar to other chronic pain patients. Evaluation of sleep state by questionnaire might be useful for diagnosis and management of TMD, because sleep disturbance decreases pain threshold and pain disturbs sleep. In addition, sleep-screening device would be useful for screening sleep related breathing disorder in dental clinic.

• Journal of Biomedical Engineering Research
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• v.35 no.4
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• pp.75-80
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• 2014

This paper proposed a method that can automatically classify sleep apnea by using features extracted from pulse rate variability(PRV) signals induced from piezo snoring sensor for patients with obstructive sleep apnea(OSA). We have extracted eight features(NN, SDNN, RMSSD, NN10, NN50, LF, HF and LF/HF ratio) based on time and frequency analyses of PRV. Sleep apnea was classified by a linear discriminant analysis(LDA). A performance was evaluated using snore recordings from 13 patients with OSA (ages: $54.5{\pm}10.5$ years, body mass index: $26.3{\pm}2.5kg/m^2$, apnea-hypopnea index: $19.2{\pm}6.0/h$). The sensitivity and specificity were $78.9{\pm}0.9%$ and $78.9{\pm}0.9%$ for training set and $77.7{\pm}10.9%$ and $79.0{\pm}2.8%$ for test set, respectively. Our study demonstrated the feasibility of implementing a piezo snoring sensor based on a portable device as a simple and cost-effective solution for contributing to the OSA screening.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.5
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• pp.171-180
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• 2023

The three major sleep disorders in Korea are snoring, sleep apnea, and insomnia. Lack of sleep is the root of all diseases. Some of the most serious potential problems associated with sleep deprivation are cardiovascular problems, cognitive impairment, obesity, diabetes, colitis, prostate cancer, etc. To solve these problems, the Korean government provided low-cost national health insurance benefits for polysomnography tests in July 2018. However, insomnia patients still have problems getting treated in terms of time, space, and economic perspectives. Therefore, it would be better for insomnia patients to be allowed to test at home. The measuring device can measure six biosignals (eye movement, tossing and turning, body temperature, oxygen saturation, heart rate, and audio). A gyroscope sensor (MPU9250, InvenSense, USA) was used for eye movement, tossing, and turning. The input range of the sensor was in 258°/sec to 460°/sec, and the data range was in the input range. Body temperature, oxygen saturation range, and heart rate were measured by a sensor (MAX30102, Analog Devices, USA). The body temperature was measured in 30 ℃ to 45 ℃, and the oxygen saturation range was 0% for the unused state and 20 % to 90 % for the used state. The heart rate measurement range was in 40 bpm to 180 bpm. The measurement of audio signal was performed by an audio sensor (AMM2742-T-R, PUIaudio, USA). The was -42 dB ±1 dB frequency range was 20 Hz to 20 kHz. The measured data was successfully received in wireless network conditions. The system configuration was consisted of a PC and a mobile app for bio-signal measurement and data collection. The measured data was collected by mobile phones and desktops. The data collected can be used as preliminary data to determine the stage of sleep and perform the screening function for sleep induction and sleep disturbances. In the future, this convenient sleep measurement device could be beneficial for treating insomnia.

• Journal of agricultural medicine and community health
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• v.30 no.2
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• pp.213-225
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• 2005

Objectives: This study was carried out to evaluate the socio-demographic, style of living, and health related behaviors of community residents between urban and rural areas in Jeonbuk province. Methods: The data was obtained from 2,086 residents who lived in Jeonbuk province from July to September 2001 which included 1,238 residents in urban area and 848 residents in rural area. Results: The results were as follows; There were significant differences socio-demographic characteristics including occupation and educational level of the populations between urban and rural areas. In the urban populations, housewives, professional classes, and people who received collage graduate education were significantly higher, people who were engaged in the primary industries and lower educational classes(below middle school), in contrast, were significantly lower than in the rural populations. In the practice of health behavior, consumption of health foods and amount of exercise in urban populations were significantly higher than in the rural, whereas the state of health, prevalence of chronic disease, Body Mass Index(BMI), consumption of ethanol and cigarette, and hours of sleep were not significantly different between the urban and rural populations. In the eating habits, rate of surfeit, vaccination of hepatitis B, and screening of cancer in the urban populations were significantly higher than in the rural, rate of regular meal, taste of salted food, consumption of vegetables, in contrast. were significantly lower than in the rural. But, consumption of coffee, intake of meat, and periodic medical checkup were not significantly different between urban and rural communities. Logistic regression analysis revealed that the factors associated with subjective thought on health state were sex, age, occupation, educational level, exercise, and consumption of coffee. Logistic regression analysis to find related factor with prevalence of chronic disease was conducted. As a results, age, educational level, BMI, intake of meat, screening of cancer, and consumption of health foods were related to prevalence of chronic disease. Conclusions: The study showed that special attention to device the health plan are necessary and it suggested that consideration of regional differences are required to provide the community health projects.