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Open AccessArticle

Oxygen Saturation and RR Intervals Feature Selection for Sleep Apnea Detection

1
Institute for Technological Development and Innovation in Communications, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria 35017, Spain
2
Department of Physics, Humboldt-Universitat zu Berlin, Berlin 10115, Germany
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Pulmonary Medicine Department, Hospital Universitario de Gran Canaria Dr. Negrın, Las Palmas de Gran Canaria 35010, Spain
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Sleep Center, Charité Universitatsmedizin, Berlin 10117, Germany
*
Author to whom correspondence should be addressed.
Academic Editor: Kevin H. Knuth
Entropy 2015, 17(5), 2932-2957; https://doi.org/10.3390/e17052932
Received: 3 December 2014 / Revised: 30 April 2015 / Accepted: 4 May 2015 / Published: 7 May 2015
(This article belongs to the Special Issue Entropy and Cardiac Physics)
A diagnostic system for sleep apnea based on oxygen saturation and RR intervals obtained from the EKG (electrocardiogram) is proposed with the goal to detect and quantify minute long segments of sleep with breathing pauses. We measured the discriminative capacity of combinations of features obtained from RR series and oximetry to evaluate improvements of the performance compared to oximetry-based features alone. Time and frequency domain variables derived from oxygen saturation (SpO2) as well as linear and non-linear variables describing the RR series have been explored in recordings from 70 patients with suspected sleep apnea. We applied forward feature selection in order to select a minimal set of variables that are able to locate patterns indicating respiratory pauses. Linear discriminant analysis (LDA) was used to classify the presence of apnea during specific segments. The system will finally provide a global score indicating the presence of clinically significant apnea integrating the segment based apnea detection. LDA results in an accuracy of 87%; sensitivity of 76% and specificity of 91% (AUC = 0.90) with a global classification of 97% when only oxygen saturation is used. In case of additionally including features from the RR series; the system performance improves to an accuracy of 87%; sensitivity of 73% and specificity of 92% (AUC = 0.92), with a global classification rate of 100%. View Full-Text
Keywords: sleep apnea; RR intervals; oxygen saturation; feature selection sleep apnea; RR intervals; oxygen saturation; feature selection
MDPI and ACS Style

Ravelo-García, A.G.; Kraemer, J.F.; Navarro-Mesa, J.L.; Hernández-Pérez, E.; Navarro-Esteva, J.; Juliá-Serdá, G.; Penzel, T.; Wessel, N. Oxygen Saturation and RR Intervals Feature Selection for Sleep Apnea Detection. Entropy 2015, 17, 2932-2957.

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