A New Algorithm to Score Apnea/Hypopnea Events based on Respiratory Effort Signal and Oximeter Sensors
Author:
Funder
Universidad Nacional de San Juan
CONICET
Publisher
Springer Science and Business Media LLC
Subject
Biomedical Engineering,General Medicine
Link
https://link.springer.com/content/pdf/10.1007/s40846-020-00549-0.pdf
Reference22 articles.
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2. Koley, B. L., & Dey, D. (2013). Automatic detection of sleep apnea and hypopnea events from single channel measurement of respiration signal employing ensemble binary SVM classifiers. Measurement: Journal of the International Measurement Confederation, 46(7), 2082–2092. https://doi.org/10.1016/j.measurement.2013.03.016.
3. Liao, P., Yegneswaran, B., Vairavanathan, S., Zilberman, P., & Chung, F. (2009). Postoperative complications in patients with obstructive sleep apnea: A retrospective matched cohort study. Canadian Journal of Anesthesia/Journal canadien d’anesthésie, 56(11), 819–828. https://doi.org/10.1007/s12630-009-9190-y
4. Saint Martin, M., Roche, F., Thomas, T., Collet, P., Barthélémy, J. C., & Sforza, E. (2015). Association of body fat composition and obstructive sleep apnea in the elderly: A longitudinal study. Obesity (Silver Spring, Md.), 23(7), 1511–1516. https://doi.org/10.1002/oby.21121.
5. Senaratna, C. V., Perret, J. L., Lodge, C. J., Lowe, A. J., Campbell, B. E., Matheson, M. C., & Dharmage, S. C. (2017). Prevalence of obstructive sleep apnea in the general population: A systematic review. Sleep Medicine Reviews, 34, 70–81. https://doi.org/10.1016/j.smrv.2016.07.002.
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