• Sleep Medicine and Psychophysiology
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• v.23 no.2
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• pp.93-96
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• 2016

Obstructive sleep apnea syndrome (OSAS) has negative effects on health, including increased mortality, risk of cardiovascular disease, and neurocognitive difficulties. OSAS is common in obese patients and obesity is an important risk factor of OSAS. A 41-year-old female OSAS patient with severe obesity (body mass index [BMI] ${\geq}35$) who failed dietary weight loss underwent bariatric surgery. After surgery, there were improvements in BMI (from 36.9 to $31.7kg/m^2$) and polysomnographic data, including the apnea-hypopnea index (from 25.1 to 11.2 events/hr) and minimum SaO2 (from 69 to 82%). This case demonstrates that bariatric surgery may be an effective therapeutic option to reduce sleep-disordered breathing in severely obese patients with moderate OSAS. Bariatric surgery as a treatment option for OSAS should be considered in OSAS patients with severe obesity who failed dietary weight loss.

• Korean Journal of Clinical Laboratory Science
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• v.39 no.3
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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.

• Journal of Biomedical Engineering Research
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• v.35 no.6
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• pp.211-218
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• 2014

The objective of this research is to develop an automatic algorithm based on electrocardiogram (ECG) to estimate slow-wave sleep (SWS). An algorithm is based on 7 indices extracted from heart rate on ECG which simultaneously recorded with standard full night polysomnography from 31 subjects. Those 7 indices were then applied to independent component analysis to extract a feature that discriminates SWS and other sleep stages. Overall Cohen's kappa, accuracy, sensitivity and specificity of the algorithm to detect 30s epochs of SWS were 0.52, 0.87, 0.70 and 0.90, respectively. The automatic SWS detection algorithm could be useful combining with existing REM and wake estimation technique on unattended home-based sleep monitoring.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.1
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• pp.21-26
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• 2017

In this paper, we suggest an automated sleep scoring method using machine learning algorithms on accelerometer data from a wristband device. For an experiment, 36 subjects slept for about eight hours while polysomnography (PSG) data and accelerometer data were simultaneously recorded. After the experiments, the recorded signals from the subjects were preprocessed, and significant features for sleep stages were extracted. The extracted features were classified into each sleep stage using five machine learning algorithms. For validation of our approach, the obtained results were compared with PSG scoring results evaluated by sleep clinicians. Both accuracy and specificity yielded over 90 percent, and sensitivity was between 50 and 80 percent. In order to investigate the relevance between features and PSG scoring results, information gains were calculated. As a result, the features that had the lowest and highest information gain were skewness and band energy, respectively. In conclusion, the sleep stages were classified using the top 10 significant features with high information gain.

• Sleep Medicine and Psychophysiology
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• v.2 no.1
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• pp.13-22
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• 1995

To provide the physician with adequate information to diagnose and treat sleepwalking and sleep terrors, the author reviewed clinical features, epidemiology, causative and precipitating factors, polysomnography, diagnosis, differential diagnosis, and treatment for these disorders. Sleepwalking and sleep terrors have been defined as disorders of arousal that occur early in the night and have their onset during stage 3 or 4 sleep. In both disorders, patients are difficult to arouse, and complete amnesia or minimal recall of the episode is frequent. Genetic, developmental, and psychological factors have been identified as causes of both sleepwalking and sleep terrors. Sleepwalking and sleep terrors typically begin in childhood or early adolescence and are usually outgrown by the end of adolescence. When sleepwalking or sleep terrors have a post-pubertal onset or continue to adulthood, psychopathology is a more significant causative factors. The behavior that occur from deep slow-wave sleep can be painful or dangerous to the individual and/or disturbing to those close to that individual. The assessment of patients suspected of having these conditions requires a thorough medical and sleep history. The most important consideration in managing patients with sleepwalking or sleep terrors episodes is protection from injury.

• Sleep Medicine and Psychophysiology
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• v.4 no.1
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• pp.89-95
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• 1997

The authors studied 5 cases of idiopathic CNS hypersomnia who visited Division of Sleep Studies, Seoul National University Hospital in 1995. Detailed medical history was taken and nocturnal polysomnography(NPSG), multiple sleep latency test(MSLT) and human leukocyte antigen(HLA) typing were performed. Neither cataplexy nor hypnagogic hallucination was reported in all cases and in NPSGs, there were tendencies of increased sleep period time and decreased slow wave sleep time. In MSLT, all the subjects showed average sleep latencies less than 8 minutes without sleep-onset rapid eye movement period(SOREMP). In HLA typing, some correlation between idiopathic CNS hypersomnia and HLA DR4 was observed. In contrast to previous reports, overall treatment response with methylphenidate was remarkable. Therefore, the authors suggest that patients suspected of idiopathic CNS hypersomnia be actively evaluated and treated with rather optimistic perspective.

• Sleep Medicine and Psychophysiology
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• v.15 no.1
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• pp.5-11
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• 2008

Sleep is an essential process maintaining the life cycle of the human. In parallel with physiological, cognitive, subjective, and behavioral changes that take place during the sleep, there are remarkable changes in the electroencephalogram (EEG) that reflect the underlying electro-physiological activity of the brain. However, analyzing EEG and relating the results to clinical observations is often very hard due to the complexity and a huge data amount. In this article, I introduce several linear and non-linear tools, developed to analyze a huge time series data in many scientific researches, and apply them to EEG to characterize various sleep states. In particular, the spectral analysis, detrended fluctuation analysis (DFA), and synchrony analysis are administered to EEG recorded during nocturnal polysomnography (NPSG) processes and daytime multiple sleep latency tests (MSLT). I report that 1) sleep stages could be differentiated by the spectral analysis and the DFA ; 2) the gradual transition from Wake to Sleep during the sleep onset could be illustrated by the spectral analysis and the DFA ; 3) electrophysiological properties of narcolepsy could be characterized by the DFA ; 4) hypnic jerks (sleep starts) could be quantified by the synchrony analysis.

• Journal of Korean Academy of Nursing
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• v.43 no.1
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• pp.81-90
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• 2013

Purpose: This study was done to test structural equation modeling of health-related quality of life (QOL) of men with obstructive sleep apnea in order to identify parameters affecting QOL and provide guidelines for interventions and strategies to improve QOL in these patients. Methods: Model construction was based on 'The conceptual model of patient outcome in health-related QOL' by Wilson and Cleary, using the variables; age, physiological factors, social support, cognitive appraisal, symptoms and QOL. Participants were 201 adult male patients recruited at a tertiary university hospital in Seoul. Data were collected via questionnaires, polysomnography, and clinical records. Results: Age and symptoms directly influenced QOL. Social support and cognitive appraisal about sleep did not have a direct influence on QOL, but indirectly affected it via symptoms. QOL was lower in patients who were younger and had more severe symptoms. Symptoms were more severe for patients with lower social support and more dysfunctional cognitive appraisal. When social support was lower, cognitive appraisal was more dysfunctional. Conclusion: These results suggest it is necessary to not only manage symptoms, but also apply interventions to increase social support and cognitive appraisal about sleep in order to increase QOL in patients with obstructive sleep apnea.

• Safety and Health at Work
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• v.7 no.2
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• pp.120-123
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• 2016

Background: To assess in a laboratory setting the ability to stay awake in a sample of workers of an Italian hospital and to investigate the association between that ability and the risk of occupational injury. Methods: Nine workers at the University Hospital of Udine who reported an occupational injury in the study period (cases), and seven noninjured workers (controls) underwent a polysomnography and four 40-minute maintenance of wakefulness tests (MWT). Differences in sleep characteristics and in wakefulness maintenance were assessed using Wilcoxon's rank sums tests and Fisher's exact tests. Results: Controls had greater sleep latency, lower total sleep time, fewer leg movements, and a higher percentage ratio of cycling alternating pattern, were more likely not to fall asleep during the MWT and were less likely to have two or more sleep onsets. Although not all the differences reached statistical significance, cases had lower sleep onset times in Trials 1-3. Conclusion: In the literature, the evidence of an association between MWT results and real life risk of accidents is weak. Our results suggest a relationship between the MWT results and the risk of injury among hospital workers.

• Journal of Sensor Science and Technology
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• v.27 no.6
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• pp.407-413
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• 2018

Most sleep apnea patients exhibit severe snoring, and long-lasting sleep apnea may cause insomnia, hypertension, cardiovascular diseases, stroke, and other diseases. Although polysomnography is the typical sleep diagnostic method to accurately diagnose sleep apnea by measuring a variety of bio-signals that occur during sleep, it is inconvenient as the patient has to sleep with attached electrodes at the hospital for the diagnosis. In this study, a diagnostic pillow is designed to measure respiration, heart rate, and snoring during sleep, using only one polyvinylidene fluoride (PVDF) sensor. A PVDF sensor with piezoelectric properties was inserted into a specially made instrument to extract accurate signals regardless of the posture during sleep. Wavelet analysis was used to identify the extractability and frequency domain signals of respiration, heart rate, and snoring from the signals generated by the PVDF sensor. In particular, to separate the respiratory signal in the 0.2~0.5 Hz frequency region, wavelet analysis was performed after removing 1~2 Hz frequency components. In addition, signals for respiration, heart rate, and snoring were separated from the PVDF sensor signal through a Butterworth filter and median filter based on the information obtained from the wavelet analysis. Moreover, the possibility of measuring sleep apnea from these separated signals was confirmed. To verify the usefulness of this study, data obtained during sleeping was used.