• Korean Journal of Clinical Laboratory Science
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• v.53 no.4
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• pp.333-341
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• 2021

Benzodiazepines (BDZs) drugs act on the GABA_A receptor, function as nerve suppressors, and are used to treat anxiety, insomnia, and panic disorder. We analyzed the data of 30 individuals to determine any differences in the sleep-electroencephalogram findings among individuals varying in age, benzodiazepine use, and duration of benzodiazepine use. Comparisons between users and non-users of benzodiazepines, short-term and long-term users, older and younger users, and older short-term and older long-term users, were achieved using electroencephalographic findings obtained through polysomnography. The parameters evaluated included sleep latency, sleep efficiency, sleep-stage percentages, number of sleep spindles, and average frequency of sleep-spindle. The difference between benzodiazepine users and non-users was significant with respect to sleep-stage percentages and average frequency of sleep-spindle. Older and younger users differed significantly with respect to sleep efficiency and sleep-stage percentages, whereas significant difference for sleep efficiency was obtained between long-term and short-term users. Taken together, our results indicate that BDZ consumption suppresses slow-wave sleep and increases the frequency of sleep spindles.

• 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.

• The Journal of the Korea Contents Association
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• v.11 no.4
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• pp.27-32
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• 2011

This paper presents a methodology for classifying sleep disturbance using electroencephalogram (EEG) signal at sleep stage 2 and principal component analysis. For extracting initial features, fast Fourier transforms(FFT) were carried out to remove some noise from EEG signal at sleep stage 2. In the second phase, we used principal component analysis to reduction from EEG signal that was removed some noise by FFT to 5 features. In the final phase, 5 features were used as inputs of NEWFM to get performance results. The proposed methodology shows that accuracy rate, specificity rate, and sensitivity were all 100%.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.5
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• pp.2909-2913
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• 2013

Background: The aim of this study was to determine factors affecting sleep quality of 100 patients with advanced stage lung cancer. Methods and Results: it was a descriptive study. A variety of assessment tools were used to provide sleep scores to examine the relation between adverse effects caused by the treatment (nausea, vomiting, fatigue) and sleep quality. As a result, no statistically significant relation between coughing and respiratory problems of patients, or existing depression, and average sleep quality score was found (KW:0.872, p=0.646, KW: 3.174, p=0.205, u: 441.000 p=0.916). It was revealed that nausea and loss of appetite experienced also did not affect the sleep quality score (p>0.05), whereas problems such as vomiting and fatigue did exert effects (p<0.01). Conclusions: Patients with advanced stage lung cancer suffer from sleep problems and cancer related symptoms also affect their sleep quality negatively. Nurses should plan interventions that can control symptoms such as pain, vomiting and fatigue, which affect the sleep of patients.

• Sleep Medicine and Psychophysiology
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• v.1 no.2
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• pp.163-171
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• 1994

Objectives: Symptoms of sleep apnea consisted of insomnia, excessive sleepiness during daytime, impaired cognitive functions and emotional disturbances. It was not so well known how these symptoms are correlated with various parameters of sleep and respiration, and what kind of psychophysiological processes are involved in development of these symptoms. Methods: In sixty patients with sleep apnea, sleep and respiration were studied by polysomnography of one night, also symptoms of sleep apnea were evaluated with the scales of insomnia, daytime sleepiness, emotional disturbance and cognitive impairment We studied correlations between apnea symptoms, and various parameters of sleep and respiration such as sleep efficiency, number severity of apnea, $O_2$ desaturation and number of snoring. Results: The result showing significant correlations are as follows. The patients with better sleep in insomnia scale showed more number of apnea, particularly more central type, and much more snoring in stage 3 sleep and mild desaturation of $O_2$. Excessive sleepiness during daytime correlated significantly with stage 1 sleep and its snoring, but correlated negatively with stage 2 sleep. However, no significant correlation was found with degree of $O_2$ desaturation. Emotional disturbance was more apparent in the patients with severe $O_2$ desaturation and smaller amounts of stage 4 sleep. Cognitive function was more impaired in cases of more REM sleep and less apnea. Conclusions: Symptoms of sleep apnea may occur through different causes and processes. The evaluation of apnea symptoms may be helpful to understand in some degree the condition of sleep and respiration during sleep in clinical setting.

• Journal of Korea Multimedia Society
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• v.24 no.8
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• pp.1149-1160
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• 2021

Lack of sleep time increases risks of fatigue, hypomnesis, decreased emotional stability, indigestion, and dementia. The risks can be reduced by providing eyelid-warming, inducing sleep and improving sleep quality. However, effective warming temperature to an person varies depending on physical condition and the individual. The various types of frequencies can be identified in brain wave from a person and amount of frequencies is also changed continuously before and after sleep. Therefore we can identify the user's sleep stage with brain wave, namely EEG. Effective sleep induction is possible if warming temperature to a person is controlled based on EEG. In this paper, we propose customized warming control techniques based on EEG for a efficient and effective sleep induction. As an experiment, sleep induction effects of standard sleep mask and customized temperature control techniques sleep mask are compared. EEG data and warming temperature were measured in 100 experiments. At customized warming control techniques, experiments showed that the ratio of alpha and theta waves increased by 3.21%p and the time to sleep decreased by 85 seconds. It will contribute to effective sleep induction and performance verification methods in customized sleep mask systems.

• Journal of Korea Multimedia Society
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• v.22 no.12
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• pp.1491-1499
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• 2019

Insufficient sleep time and bad sleep quality causes many illnesses and it's research became more and more important. The most common method for measuring sleep quality is the polysomnography(PSG). The PSG is a test used to diagnose sleep disorders. The most common PSG data is obtained from the examiner, which attaches several sensors on a body and takes sleep overnight. However, most of the sleep stage classification in PSG are low accuracy of the classification. In this paper, we have studied algorithm for sleep level classification based on machine learning which can replace PSG. EEG, EOG, and EMG channel signals are studied and tested by using CNN algorithm. In order to compensate the performance, a mixed model using both CNN and DNN models is designed and tested for performance.

• Sleep Medicine and Psychophysiology
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• v.4 no.1
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• pp.49-56
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• 1997

Nocturnal panic involves sudden awakening from sleep in a state of panic characterized by various somatic sensation of sympathetic arousal and intense fear. Many(18-71%) of the spontaneous panic attacks tend to occur from a sleeping state unrelated to the situational and cognitive context. Nocturnal panickers experienced daytime panics and general somatic sensation more frequently than other panickers. Despite frequent distressing symptoms, these patients tend to exhibit little social or occupational impairment and minimal agoraphobia and have a high lifetime incidence of major depression and a good response to tricyclic antidepressants. Sleep panic attacks arise from non-REM sleep, late stage 2 or early stage 3. The pathophysiology and the similarity of nocturnal panic to sleep apnea, dream-induced anxiety attacks, night terrors, sleep paralysis, and temporal lobe epilepsy are discussed.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.19 no.4
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• pp.307-312
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• 2007

This study was performed to evaluate sleep efficiencies and conditions for comfortable sleep based on the analysis of sleep efficiency and MST under four thermals conditions ($22^{\circ}C,\;24^{\circ}C,\;26^{\circ}C,\;30^{\circ}C$). Five female subjects who have similar life cycle and sleep patterns were participated for the sleep experiment. Their age was from 20 to 22 years old. They were healthy, and had regular sleep with consistent bed and wakeup time. It was checked whether they had a good sleep before the night of experiment. Experiments were performed in an environmental chamber using thermo-hygrostat. The physiological signal (EEG) for sleep stage were obtained from C3-A2 and C4-Al electrode sites. Sleep stages were classified, then SWS latency and SWS/TST were calculated for the evaluation for sleep efficiencies on thermal conditions. As results, mean skin temperature for comfort sleeping was $34.5{\sim}35.4^{\circ}C$. Considering sleep efficiency and mean skin temperature, indoor room temperature of upper limit was $28.1^{\circ}C$.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.18 no.7
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• pp.535-540
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• 2006

This study was performed In evaluate sleep efficiencies and conditions for comfortable sleep based on the analysis of Physiological signals under variations in thermal conditions. Five female subjects who have similar life cycle and sleep patterns were participated for the sleep experiment. It was checked whether they had a good sleep before the night of experiment. EEGs were obtained from C3-A2 and C4-A1 electrode sites and EOGs were acquired from LOC (left outer canthus) and ROC (right outer canthus) for REM sleep detection. Sleep stages were classified, then TST (total sleep time), SWS (slow wave sleep) latency and SWS/TST were calculated for the evaluation of sleep efficiencies on thermal conditions. TST was defined as an amount of time from sleep stage 1 to wakeup. SWS latency was from light off time to sleep stage 3 and percentage of SWS over TST was calculated for the evaluation of sleep quality and comfort sleep under thermal conditions. As result, the condition which raise a room temperature provided comfortable sleep.