Detection and Prediction for Obstructive Sleep Apnea Recognition
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Publisher
Springer Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-16-9573-5_29
Reference14 articles.
1. Chen, L., Zhang, X., Wang, H.: An obstructive sleep apnea detection approach using kernel density classification based on single-lead electrocardiogram. J. Med. Syst. 39, 47 (2015)
2. Karamanli, H., Yalcinoz, T., Yalcinoz, M.A., Yalcinoz, T.: A prediction model based on artificial neural networks for the diagnosis of obstructive sleep apnea. Sleep Breath. 20, 509–514 (2016)
3. Mendonça, F., Mostafa, S.S., Morgado-Dias, F., Navarro-Mesa, J.L., Juliá-Serdá, G., RaveloGarcía, A.G.: A portable wireless device based on oximetry for sleep apnea detection. Computing 100, 1203–1219 (2018)
4. Hang, L.-W., Wang, H.-L., Chen, J.-H., Hsu, J.-C., Lin, H.-H., Chung, W.-S., et al.: Validation of overnight oximetry to diagnose patients with moderate to severe obstructive sleep apnea. BMC Pulm. Med. 15, 24 (2015)
5. Gutiérrez-Tobal, G.C., Kheirandish-Gozal, L., Álvarez, D., Crespo, A., Philby, M.F., Mohammadi, M., et al.: Analysis and classification of oximetry recordings to predict obstructive sleep apnea severity in children. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), MiCo—Milano Conference Center—Milan, Italy, August 25–29 2015, pp. 4540–4543 (2015)
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