Open Access
Issue |
MATEC Web of Conferences
Volume 22, 2015
International Conference on Engineering Technology and Application (ICETA 2015)
|
|
---|---|---|
Article Number | 05023 | |
Number of page(s) | 6 | |
Section | Chemical and Industrial Technology | |
DOI | https://doi.org/10.1051/matecconf/20152205023 | |
Published online | 09 July 2015 |
- Emad Malaekah, Chanakya Reddy Patti & Dean Cvetkovic. 2014. Automatic Sleep-Wake Detection using Electrooculogram Signals, IEEE Conference on Biomedical Engineering and Sciences, pp. 724–728. [Google Scholar]
- Yutao Jia. & Zhizeng Luo. “Summary of EMG Feature Extraction” Chinese Journal of Electron Devices, 30: 326–330. [Google Scholar]
- J. Virkkala, J. Hasan, A. Värri, S.-L. Himanen, & K. Müller. 2005. Automatic sleep stage classification using two channel electrooculography, Journal of Neuroscience Methods, 166: 109–115. [CrossRef] [Google Scholar]
- Kempfner J., Sorensen G. L., Sorensen H. B. D. & Jennum P. 2011. Automatic REM Sleep Detection Associated with Idiopathic REM Sleep Behavior Disorder, 33rd Annual International Conference of the IEEE EMBS Boston, Massachusetts USA, pp. 6063–6066. [Google Scholar]
- M.O. Mendez, M. Matteucci, S. Cerutti, F. Aletti & A.M. Bianchi. 2009. Sleep Staging Classification Based on HRV: Time-Variant Analysis 31st Annual International Conference of the IEEE EMBS Minneapolis, Minnesota, USA, September 2–6. [Google Scholar]
- Welch AJ. & Richardson PC. 1973. Computer sleep stage classification using heart rate data. Electroencephalography and Clinical Neurophysiology. [Google Scholar]
- E Estrada1, H Nazeran1, 2, P Nava1, K Behbehani, J Burk, & E Lucas. 2005. Itakura Distance: A Useful Similarity Measure between EEG and EOG Signals in Computer-aided Classification of Sleep Stages, Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference Shanghai, China, September, pp: 1–4. [Google Scholar]
- Ganesh Balakrishnan, Divya Burli, John R. Burkg, Edgar A. Lucasg. & Khosrow Behbehani. 2005. Comparison of a Sleep Quality Index between Normal and Obstructiv Sleep Apnea Patients, Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference Shanghai, China, September, pp: 1–4. [Google Scholar]
- Sana Tmar-Ben Hamida & Beena Ahmed. 2013. Computer based Sleep Staging: Challenges for the Future, 2013 IEEE GCC Conference and exhibition, November 17-20, Doha, Qatar, [Google Scholar]
- L.W. Hang, B.L. Su, & C.-W. Yen, Detecting Slow Wave Sleep via One or Two Channels of EEG/EOG Signals, REM, 17. [Google Scholar]
- Chen Weidong, Li Xin, Liu Jun, Hao Yaoyao, Liao Yuxi, Su Yu, Zhang Shaomin. & Zheng Xiaoxiang. Mathematical morphology based electro-oculography recognition algorithm for human-computer interaction, Journal of Zhejiang University (Engineering Science), 45: 644–649. [Google Scholar]
- Sleep Heart Health Study. 1998. Methods for obtaining and analyzing unattended polysomnography data for a multicenter study. Sleep Heart Health Research Group. [Google Scholar]
- Redline S, Sanders MH, Lind BK, Quan SF, Iber C, Gottlieb DJ, Bonekat WH, Rapoport DM, Smith PL, Kiley JP. Sleep. Nov 1; 21(7):759–67. [Google Scholar]
- Redline, S., et al. “Sleep Heart Health Study.” National Sleep Research Resource. Web. http://sleepdata.org/datasets/shhs [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.