Detection of sleep apnea events via tracking nonlinear dynamic cardio-respiratory coupling from electrocardiogram signals

Obstructive sleep apnea (OSA) is a common sleep disorder that causes increasing mortality risk and affects the quality of life of approximately 6.62% of the total US population. Timely detection of sleep apnea events is vital for the treatment of OSA. In this paper, we present a novel approach based on extracting the quantifiers of nonlinear dynamic cardio-respiratory coupling from electrocardiogram (ECG) signals to detect sleep apnea events. The quantifiers of the cardio-respiratory dynamic coupling were extracted based on recurrence quantification analysis (RQA), and a battery of statistical data mining techniques were executed to enhance the accuracy of OSA detection. This approach leads to a more cost-effective and convenient means for screening for OSA, compared to traditional polysomnography (PSG) methods. The results of tests using data from the PhysioNet Sleep Apnea database suggest an excellent quality of OSA detection based on a thorough comparison of multiple models, using model selection criteria for validation data: Sensitivity (91.93%), Specificity (85.84%), Misclassification (11.94%) and Lift (2.7).