• Quality Improvement in Health Care
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• v.1 no.1
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• pp.124-131
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• 1994

Polysomnography is an essential methodology for diagnosing and following up sleep disorders and doing researches on human sleep. Sleep medicine, mainly with the utilization of polysomnographic techniques, has developed itself as one of the promising fields in the 21st century medicine. Korea is not an exception in importing and developing sleep medicine into the conventional medicine. However, it still remains to be clarified what polysomnography is for and how it should be done, considering the relatively recent introduction of sleep medicine into Korea. The author, being a board-certified sleep medicine specailist, having experienced spreading out sleep medicine within Korea for the past four years, and having recently set up a major sleep study facility in Korea at Seoul National University Hospital, attempts in this introductory critical article to review the essential issues related to quality assurance in polysomnographic study of human sleep. Also, unconditional introduction of "automated" sleep scoring system, which has been found to have significantly reduced reliability in various studies including the author's own, is critically reviewed. The author suggests that quality assurance and training program should be initiated and established by a responsible sleep medicine-related organization such as the Korean Association of Sleep Medicine and Psychophysiology.

• 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.