• Sleep Medicine and Psychophysiology
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• v.7 no.2
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• pp.115-119
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• 2000

Objectives Summary: A 20-year-old man was presented with a history of difficult waking for 10 years. He suffered from morning headache, chronic fatigue and mild daytime sleepiness but had no history of irresistible sleep attack, cataplexy, hypnagogic hallucination or sleep paralysis. Methods: Night polysomnography (PSG), multiple sleep latency test (MSLT) and HLA-typing were carried out. Results: The PSG showed short sleep latency (4.0 min) and REM latency (2.5 min), increased arousal index (15.7/hour), periodic limb movements during sleep (PLMS index=8.1/hr) with movement arousal index 2.1/hr and normal sleep efficiency (97.5%). The MSLT revealed normal sleep latency (15 min 21 sec) and 4 times sleep-onset REM (SOREM). HLA-typing showed DQ6- positive, that corresponded at the genomic level to the subregion DQB1*0601, which was different from the usual locus in narcolepsy patients (DQB1*0602 and DQA1*0102). Conclusion: Differential diagnosis should be made with circadian rhythm disorder and other causes of primary waking disorder. The possibility of a variant type of narcolepsy could be suggested with an unusual clinical manifestation and a new genetic marker.

• Korea Science and Art Forum
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• v.35
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• pp.145-155
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• 2018

The goal of the study is to provide a new type of audio-visualization method through case analysis and work production based on Polysomnography(PSG) data that is difficult to interpret or not familiar to the public. Most art works are produced with conscious actions during waking hours. On the other hand, during sleep, we get into the world of unconsciousness. Therefore, through the experiment, want to discover if could get something new when we were in the subconscious state, and if so, wondered what kind of art could be made through it. The study method is to consider definition of sleep and sleep data first. The sleep data were classified into normal group and Narcolepsy, Insomnia, and sleep apnea by focusing on sleep disorder graphs that is measured by sleep polygraph. After that, I refined and converted the acquired biometric data into a text-based script. The degree of sleep in the text form of the script was rendered as a 3D animated image using Maya. In addition, the heart rate data script was transformed into a midi format, and the audition was implemented in the garage band. After Effects combines the image and sound to create four single channel images of 3 minutes and 20 seconds each. As a result of the research, I made an opportunity for anyone easy to understand the results, having difference with the normal data, through art instead of using difficult medical term. It also showed the possibility of artistic expression even when conscious actions did not occur. Through the results of this research, I expect the expansion and diversity of artistic audiovisual expression of biometric data.

• Korean Journal of Clinical Laboratory Science
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• v.47 no.4
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• pp.216-224
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• 2015

This study aims to identify the prevalence of sleep related disease in those who experienced car accidents caused by drowsy driving. To this end, a survey of usual sleep habits, polysomnography, and multiple sleep latency tests were conducted in 34 persons who experienced an accident after normal sleep (Group 1), 22 persons who experienced an accident after abnormal sleep (Group 2), and 17 persons who was proven to be normal as a result of polysomnography and had no accident (Group 3). In all, 192 persons responded to the preliminary survey and the results were compared and analyzed. Crossover analysis was conducted to test the homogeneity of statistical characteristics, and the physical characteristics by age were analyzed. In the survey of sleeping habits, there was a significance between groups in how often they woke up while asleep (p<0.01), how difficult it was to go back to sleep again after waking up from sleep (p<0.05), how early they woke up in the morning (p<0.05), how difficult it was to get up in the morning (p<0.05), how sleepy they felt in the daytime (p<0.01), and how tired they felt in the daytime (p<0.01). Furthermore, among 56 subjects who had an accident during drowsy driving, 94.6% (53 persons) were found to have sleep related diseases. This suggests that car accidents during drowsy driving is not simply caused by temporary lack of sleep but by sleep related diseases even when sleep is adequate, leading to car accidents. Therefore, this study is significant identifying the association between car accidents during drowsy driving and sleep related disorders. Furthermore, the data would be considered basic to prepare social measures against drowsy driving related to such sleep related disorders.