지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한전자공학회 IEIE Transactions on Smart Processing & Computing IEIE Transactions on Smart Processing & Computing Vol.6 No.1
- 발행연도
- 2017.2
- 수록면
- 21 - 26 (6page)
이용수
초록· 키워드
오류제보하기
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.
목차
Abstract
1. Introduction
2. Methods
3. Result
4. Conclusion
References
참고문헌 (10)
참고문헌 신청
Morillo, Daniel Sanchez, An accelerometer-based device for sleep apnea screening. IEEE transactions on information technology in biomedicine, vol. 14, no. 2, pp. 491-499, 2010. Article (CrossRef Link)
Karantonis, Dean M., Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring. IEEE transactions on information technology in biomedicine, vol 10, No. 1, pp. 156-167, 2006. Article (CrossRef Link)
Jean-Louis, Girardin, Sleep estimation from wrist movement quantified by different actigraphic modalities. Journal of neuroscience methods, vol 105, No. 2, pp. 185-191, 2001. Article (CrossRefLink)
Witten, Ian H., and Eibe Frank. Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann 2005. Article, vol 2005. Article (CrossRef Link)
Cleary, John G., Leonard E. Trigg. K*: An instancebased learner using an entropic distance measure. Proceedings of the 12th International Conference on Machine learning, vol 5, pp. 108-114, 1995 Article (CrossRefLink)
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UCI(KEPA) : I410-ECN-0101-2017-569-002238392