Oxygen Saturation and RR Intervals Feature Selection for Sleep Apnea Detection

Entropy

Antonio G. Ravelo-Garc໚⁴  Jan F. Kraemer²  Juan L. Navarro-Mesa⁴  Eduardo Hernández-Pérez⁴  Javier Navarro-Esteva¹  Gabriel Juliá-Serdá¹  Thomas Penzel³  Niels Wessel² 

[1] Pulmonary Medicine Department, Hospital Universitario de Gran Canaria Dr. Negrın, Las Palmas de Gran Canaria 35010, Spain; E-Mails:;Department of Physics, Humboldt-Universitat zu Berlin, Berlin 10115, Germany; E-Mails:;Sleep Center, Charité Universitatsmedizin, Berlin 10117, Germany; E-Mail:;Institute for Technological Development and Innovation in Communications, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria 35017, Spain;E-Mails:

关键词: sleep apnea;    RR intervals;    oxygen saturation;    feature selection;   

DOI  :  10.3390/e17052932

来源: mdpi

【 摘 要 】

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%.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland