Airflow Analysis in the Context of Sleep Apnea-Reference-Cited by-同舟云学术

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Airflow Analysis in the Context of Sleep Apnea

Published:2022 Issue: Volume: Page:241-253
ISSN:0065-2598
Container-title:Advances in the Diagnosis and Treatment of Sleep Apnea
language:
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Author:

Barroso-García Verónica,Jiménez-García Jorge,Gutiérrez-Tobal Gonzalo C.,Hornero Roberto

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-06413-5_14

Reference54 articles.

1. Alonso-Álvarez, M. L., Terán-Santos, J., Ordax Carbajo, E., Cordero-Guevara, J. A., Navazo-Egüia, A. I., Kheirandish-Gozal, L., & Gozal, D. (2015). Reliability of home respiratory polygraphy for the diagnosis of sleep apnea in children. Chest, 147(4), 1020–1028. https://doi.org/10.1378/chest.14-1959

2. Álvarez, D., Cerezo-Hernández, A., Crespo, A., Gutiérrez-Tobal, G. C., Vaquerizo-Villar, F., Barroso-García, V., Moreno, F., Arroyo, C. A., Ruiz, T., Hornero, R., & del Campo, F. (2020). A machine learning-based test for adult sleep apnoea screening at home using oximetry and airflow. Scientific Reports, 10(1), 1–12. https://doi.org/10.1038/s41598-020-62223-4

3. Álvarez-Estévez, D., & Moret-Bonillo, V. (2009). Fuzzy reasoning used to detect apneic events in the sleep apnea-hypopnea syndrome. Expert Systems with Applications, 36(4), 7778–7785. https://doi.org/10.1016/j.eswa.2008.11.043

4. Avci, C., & Akbaş, A. (2015). Sleep apnea classification based on respiration signals by using ensemble methods. Bio-medical Materials and Engineering, 26, S1703–S1710. https://doi.org/10.3233/BME-151470

5. Aydoğan, O., Öter, A., Güney, K., Kıymık, M. K., & Tuncel, D. (2016). Automatic diagnosis of obstructive sleep apnea/hypopnea events using respiratory signals. Journal of Medical Systems, 40(12), 1–8. https://doi.org/10.1007/s10916-016-0624-0

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