Next Article in Journal
Maximum Entropy Method for Operational Loads Feedback Using Concrete Dam Displacement
Previous Article in Journal
Three-Stage Quantum Cryptography Protocol under Collective-Rotation Noise
Previous Article in Special Issue
A Comparison of Nonlinear Measures for the Detection of Cardiac Autonomic Neuropathy from Heart Rate Variability
Open AccessArticle

Oxygen Saturation and RR Intervals Feature Selection for Sleep Apnea Detection

Institute for Technological Development and Innovation in Communications, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria 35017, Spain
Department of Physics, Humboldt-Universitat zu Berlin, Berlin 10115, Germany
Pulmonary Medicine Department, Hospital Universitario de Gran Canaria Dr. Negrın, Las Palmas de Gran Canaria 35010, Spain
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;
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.

Show more citation formats Show less citations formats

Article Access Map by Country/Region

Only visits after 24 November 2015 are recorded.
Back to TopTop