Developing and Validating Ensemble Classifiers for At-Home Sleep Apnea Screening
Author:
Affiliation:
1. Ubiquitous and Personal Computing Lab, Faculty of Engineering, Kyoto University of Advanced Science (KUAS), Kyoto 615-8577, Japan
2. Institute of Industrial Science, The University of Tokyo, Kyoto 153-8505, Japan
Publisher
MDPI
Link
https://www.mdpi.com/2673-4591/58/1/49/pdf
Reference8 articles.
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2. Accuracy of Fitbit wristbands in measuring sleep stage transitions and the effect of user-specific factors;Liang;JMIR Mhealth Uhealth,2019
3. Berry, R.B., Brooks, R., Gamaldo, C.E., Harding, S.M., Marcus, C., and Vaughn, B.V. (2017). The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology, and Technical Specifications, American Academy of Sleep Medicine. Version 2.4 ed.
4. Support vector machine prediction of obstructive sleep apnea in a large-scale Chinese clinical sample;Huang;Sleep,2020
5. Mostafa, S.S., Mendonça, F., Morgado-Dias, F., and Ravelo-García, A. (2017, January 20–23). SpO2 based sleep apnea detection using deep learning. Proceedings of the 2017 IEEE 21st International Conference on Intelligent Engineering Systems (INES), Larnaca, Cyprus.
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