• 대한임상검사과학회지
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• 제39권3호
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• pp.264-270
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• 2007

Obstructive sleep apnea syndrome (OSAS) is defined by sleep apnea with decreased oxygen saturation, excessive snoring with daytime sleepiness, and frequent awakening during the night time sleep. The present study was performed to investigate how apnea-hypopnea, that possibly causes breathing disturbance during sleep, can affect sleep pattern in patients with OSAS. We included 115 patients (92 men, 23 women) who underwent a polysomnography from January 2006 to May 2007. As the frequency of sleep apnea-hypopnea increases, the proportion of non-rapid eye movement (REM) sleep (p<0.001), and stage I sleep (p<0.001) increased, while that of stage II sleep (p<0.001), stage III and IV sleep (p<0.01), and REM sleep (p<0.05) decreased. Furthermore, sleep apnea-hypopnea was closely correlated with REM sleep (r=0.314, p<0.001), stage I sleep (r=0.719, p<0.001), stage II sleep (p=-0.342, p<0.05), stage III and IV sleep (r=-0.414, p<0.001), and REM sleep (r=-0.342, p<0.05). Stage I sleep could account for the 51% of the variance of apnea-hyponea. Our study shows sleep apnea-hypopnea affects sleep pattern in pattern with OSAS significantly, and the change of stage I sleep is the most important factor in estimating the disturbance of sleep pattern.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1997년도 추계학술대회
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• pp.464-467
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• 1997

In this study, ECG was recorded during sleep patients with obstructive sleep apnea. We detecte(heart rate variability) signal from the ECG wa QRS detection algorithm. And we observed HRV by the power spectrum density using autoregr modeling. The experimental results were analysis sleep stage 1, sleep stage 2, sleep stage 3, sleep s sleep stage REM. In experimental result, the PSD with obstructive sleep apnea patients was distributed low frequency band except sleep step 4. These effect means that the sympathetic nervous system affected the sleep stage 1, 2, REM and the parasympathetic nervous system affected the sleep stage 3, 4 with obstructive sleep apnea patients.

• 한국산업융합학회 논문집
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• 제23권3호
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• pp.433-444
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• 2020

This study proposes a method to improve the sleep stage and efficiency estimation of sleep apnea patients using a UWB (Ultra-Wideband) radar. Motion and respiration extracted from the radar signal were used. Respiratory signal disturbances by motion artifacts and irregular respiration patterns of sleep apnea patients are compensated for in the preprocessing stage. Preprocessing calculates the standard deviation of the respiration signal for a shift window of 15 seconds to estimate thresholds for compensation and applies it to the breathing signal. The method for estimating the sleep stage is based on the difference in amplitude of two kinds of smoothed respirations signals. In smoothing, the window size is set to 10 seconds and 34 seconds, respectively. The estimated feature was processed by the k-nearest neighbor classifier and the feature filtering model to discriminate between the sleep periods of the rapid eye movement (REM) and non-rapid eye movement (NREM). The feature filtering model reflects the characteristics of the REM sleep that occur continuously and the characteristics that mainly occur in the latter part of this stage. The sleep efficiency is estimated by using the sleep onset time and motion events. Sleep onset time uses estimated features from the gradient changes of the breathing signal. A motion event was applied based on the estimated energy change in the UWB signal. Sleep efficiency and sleep stage accuracy were assessed with polysomnography. The average sleep efficiency and sleep stage accuracy were estimated respectively to be about 96.3% and 88.8% in 18 sleep apnea subjects.

• 한국감성과학회:학술대회논문집
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• 한국감성과학회 2002년도 춘계학술대회 논문집
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• pp.181-185
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• 2002

The purpose of this study was to find out the relationship between sleep physiological signals data and subjective feeling of sleep quality. Sixteen subjective were investigated and they slept on both comfortable mattress and uncomfortable mattress. Information of sleep stage is one of the most important clues for sleep quality. Polysomnography is basically the recording of sleep. The several channels of brain waves (EEG), eyes (EOG), chin movements (EMG) and heart (ECG) were monitored. Sixteen subjects spent 6 days and nights in the laboratory and the data of sleeping 7h for each of 3 nights was analyzed. Percentage of deep sleep (III and IV, sleep efficiency, WASO, stage 1 and subjective feeling of sleep quality were significantly affected with mattress types (comfortable and uncomfortable mattress). When subjects slept on comfortable beds, percentage of deep sleep and sleep efficiency were higher than those of uncomfortable bed. The percentages of wake after sleep onset and stage 1 were lower when subject slept in a comfortable bed. The subjective feeling of sleep quality agreed with the recorded sleep data also.

• 통합자연과학논문집
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• 제12권1호
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• pp.9-13
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• 2019

Recently, as the sleep disorder problem of modern people deepens, the interest towards quality of sleep is increasing. To increase the quality of modern people's sleep. This paper has suggested an LED lighting control system according to the sleep stage using PPG sensors of wearable devices. The pulse of the wrist radial artery was measured using a wearable device mounted with PPG sensor, which enables heart rate-measuring, and by using the point that heart rate lowers during stable sleep than non-sleeping, the LED lighting of indoors was controlled, which is the disturbing element when sleeping. For the performance evaluation, a 10-Fold cross analysis was conducted for performance evaluation, and a result of an average accuracy 87.02% was obtained as a result. Therefore, the LED lighting control system according to the sleep stage using a wearable device of this paper is expected to contribute to raise the quality of the user's life.

• 감성과학
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• 제6권4호
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• pp.9-14
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• 2003

수면단계는 수면감을 평가하는 데 있어서 중요한 생리지표로서 사용되어 왔다. 그러나 수면다원검사를 이용한 전통적 수면단계 분류방법은 뇌전도(electroencephalogram : EEG), 안전도(electrooculogram : EOG), 심전도(electrocardiogram : ECG), 근전도(electromyogram : EMG) 등을 종합적으로 측정하므로 수면단계를 비교적 정확히 분류할 수 있지만 피험자에게 심한 구속감을 주는 문제가 있다. 본 연구에서는, 각성상태에서 교감신경계가 지배적인 반면에 수면 중에는 부교감 신경계가 더 활동적인 점에 착안하여 수면단계를 간단히 분류할 수 있는 방법을 찾고자 수면단계에 따른 심박동변이도(heart rate variability : HRY)를 분석하였다. 이 실험에는 건강한 대학생 6명이 2일씩 전체 12회의 야간수면에 참여하였다. 수면다원검사 장치를 이용하여 피험자들이 수면을 취하고 있는 동안, EEG, EOG, ECG, EMG(턱 및 다리)를 측정하여 수면단계를 "Standard scoring system for sleep stage"에 따라 자동으로 분류하였다. 그런 뒤, 본 연구를 통하여 제작된 Sleep Data Acquisition/Analysis 시스템을 이용하여 수면다원검사 장치로부터 ECG신호만 추출하여 HRV의 전력스펙트럼을 3개의 영역[저주파수대역(low frequency : LF), 중간주파수대역(medium frequency : MF), 고주파수대역(high frequency : HF)]으로 나누어 분석하였다. 단일채널 ECG를 이용하여 수면단계별로 HRV의 LF/HF를 분석한 결과, W(wakefulness)단계가 2단계에 비하여 325%높게(p＜.05), 3단계에 비하여 628%높게(p＜.001), 4단계에 비하여 800%높게(p＜.001) 나타났으며, 4단계는 REM(rapid eye movement)단계에 비하여 427% 낮게(p＜.05), 1단계에 비하여 418% 낮게(p＜.05) 나타났다. 또한 LF/HF가 수면단계에 따라 변화하는 양상은 W, REM, 1, 2, 3, 4단계의 순으로 단조 감소하였다. 한편, 수면단계별 MF/(LF+HF)의 차이는 유의하지 않았으나 표본집단의 기술통계치를 살펴본 바 REM단계와 3단계의 평균치가 가장 높았다.치가 가장 높았다.

• 수면정신생리
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• 제6권2호
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• pp.116-125
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• 1999

Objectives: The purpose of this study is to investigate the effect of exercise load on sleep structure and stress hormone secretion during sleep. Methods: Five male physical education students were included in this study after giving their written, informed consents in the Research Institute for Sports Science at the University of Hanyang. All subjects have performed for at least 3 years in a regular aerobic exercises such as football, basketball, and running. The subjects were divided into three groups ; NOE(non-exercise), MDE(middle duration exercise), LDE(long duration excercise). MDE group maintained a total of 120 min exercise, and LDE group maintained a total of 300 min exercise by football, basketball or badminton. All subjects were acclimatized to the experimental sleep condition by spending one night under expermental conditions, including the placement of an intravenous catheter. During the subsequent night(24:00-08:00), somnopolygraphic sleep recordings were obtained, and blood for measuring growth hormone, cortisol, testosterone, and $\beta$-endorphin was collected every 120 min throughout the night. Blood samples were obtained from prominent forearm veins of subjects. Then, the samples were immediately placed in ice and centrifuged within 10 min at 3000 rpm at $4^{\circ}C$. Statistical analyses were performed using the SPSS/$PC^+$. Data were analyzed by one-way ANOVA with repeated measures. Results: No significant differences among groups were observed in sleep latency, total sleep time, stage 2 sleep, and slow wave sleep. However, daytime exercise produced significant changes in stage 1 sleep, REM sleep, stage 2 sleep latency, REM sleep latency and sleep efficiency. Stage 1 sleep, stage 2 sleep latency, and REM sleep latency significantly increased in LDE compared to those of NOE and MDE groups. But the amount of REM sleep significantly decreased in LDE. Sleep efficiency of MDE was higher than those of NOE and LDE. The blood concentrations of growth hormone, testosterone, and cortisol during night sleep were significantly lower in LDE than in NOE. $\beta$-endorphin concentrations in blood during night sleep were not different among groups. Conclusion: The daytime exercise load was significantly related to sleep structure and stress hormone secretion during night sleep. Long duration exercise showed a harmful effect on sleep structure and hormone secretion. However, middle duration exercise had a beneficial effect on sleep structure and hormone secretion during sleep.

• 수면정신생리
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• 제10권1호
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• pp.52-60
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• 2003

목 적：1단계 수면은, 입면 시점과 관련하여 수면다원기록의 해석에 중요한 정보를 제공한다. 1단계 수면은 각성 상태에서 수면 상태로의 짧은 전이 기간으로, 특징적인 지표가 없어 디지털 분석을 통한 수면 단계 결정에 어려움이 있다. 본 연구에서는, 뇌파와 안전도에 대한 디지털 분석을 통하여 1단계 수면을 자동으로 탐지하는 프로그램을 개발하고자 하였다. 방 법：야간수면다원기록 중 검사 시작 시점부터 2단계 수면이 출현하기 이전의 자료를 분석하였다. 뇌파의 스펙트럼 분석을 통해 알파파와 세타파의 상대 파워를 계산하였고, 알파파의 상대 파워가 50% 이하, 세타파의 상대 파워가 23% 이상일 경우 1단계 수면 판정의 기준 변수로 하였다. 또 안구운동의 지속시간이 1.5초에서 4초 사이에 있는 경우에 느린 안구운동(SEM)으로 판정하고 1단계 수면 판정의 기준변수로 하였다. 이 들 세 기준 변수들을 고려하여 해당 판독단위에 대해 각성 혹은 1단계 수면으로 최종 판정하였다. 결 과：연구 대상자는 7명으로 모두 남성이었으며, 23세였다. 개발된 프로그램을 이용하여 169개의 판독단위를 분석하였다. 기준과의 일치도는 79.3%였으며, 카파값은 0.586이고, 통계적으로 유의하였다. 느린 안구운동은 169개의 판독단위 중 54개(32%)에서 나타났으며, 70.4%의 일치도를 보였다. 결 론：기존 연구의 디지털 분석을 통한 수면 단계 판정의 일치도는 70%이다. 본 프로그램의 일치도 79.3%는 기존 연구 결과에 비해 향상된 것이며, 본 프로그램이 1단계 수면 판정에 유용하다고 판단된다. 뇌파 외에 안전도를 고려한 다중적 접근이 일치도 향상에 기여했을 것으로 생각되며, 1단계 수면 판정에 있어 안전도의 중요성을 확인할 수 있었다.

• 한국멀티미디어학회논문지
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• 제21권5호
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• pp.609-616
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• 2018

In this paper, we designed a multimodal bio-signal measurement system to observe changes in the brain nervous system and vascular system during sleep. Changes in the nervous system and the cerebral blood flow system in the brain during sleep induce a unique correlation between the changes in the nervous system and the blood flow system. Therefore, it is necessary to simultaneously observe changes in the brain nervous system and changes in the blood flow system to observe the sleep state. To measure the change of the nervous system, EEG, EOG and EMG signal used for the sleep stage analysis were designed. We designed a system for measuring cerebral blood flow changes using functional near-infrared spectroscopy. Among the various imaging methods to measure blood flow and metabolism, it is easy to measure simultaneously with EEG signal and it can be easily designed for miniaturization of equipment. The sleep stage was analyzed by the measured data, and the change of the cerebral blood flow was confirmed by the change of the sleep stage.

• 전자공학회논문지
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• 제50권10호
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• pp.207-216
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• 2013

본 연구의 목적은 수면상태 분석을 위한 분류기를 설계해줌과 동시에 생체신호를 기반으로 하여 수면상태 판별에 유효한 주요 특징벡터들을 추출함에 있다. 수면은 인간의 삶에 중요한 영향을 끼친다. 따라서 사람들이 수면부족 혹은 수면장애를 겪게 되면 집중력 감퇴, 인지기능 장애 등의 문제를 가질 우려가 생기게 되므로, 수면단계 판별에 관한 많은 연구들이 이루어지고 있다. 본 연구에서는 피험자가 수면을 취하는 동안 피험자의 생체신호를 획득하였다. 획득 된 생체신호로부터 필터링 등의 전처리 과정을 통하여 특징들을 추출하여 주었다. 추출된 특징들은 유전 알고리즘과 신경망을 결합하여 만든 새로운 알고리즘의 입력으로 사용되었으며, 알고리즘은 수면단계 분석을 위하여 높은 가중치를 가지는 특징을 선택하여 주었다. 이에 따른 결과로 뇌파 신호와 심전도 신호 모두 사용 시 알고리즘의 정확도는 약 90.26%가 나왔으며, 선택되어진 특징은 뇌파 신호의 ${\alpha}$파와 ${\delta}$파의 주파수 파워와 심전도 신호의 SDNN(Standard deviation of all normal RR intervals)이다. 선택된 특징은 수면상태를 분류하는데 중요한 역할을 함을 알고리즘을 반복적으로 수행하여 확인하였고, 이 연구는 추후 수면장애의 진단 혹은 수면분석의 지침을 만드는데 사용가능할 것으로 사료된다.