Detection of sleep apnea events via tracking nonlinear dynamic cardio-respiratory coupling from electrocardiogram signals
作者： Kunal Karandikar , Trung Q. Le , Akkarapol Sa-ngasoongsong , Woranat Wongdhamma , Satish T. S. Bukkapatnam
DOI: 10.1109/EMBC.2013.6611191
关键词:
摘要: Obstructive sleep apnea (OSA) is a common disorder that causes increasing risk of mortality and affects quality life approximately 6.62% the total US population. Timely detection events vital for treatment OSA. In this paper, we present novel approach based on extracting quantifiers nonlinear dynamic cardio-respiratory coupling from electrocardiogram (ECG) signals to detect events. The were extracted recurrence quantification analysis (RQA), battery statistical data mining techniques enhance OSA accuracy. This would lead cost-effective convenient means screening OSA, compared traditional polysomnography (PSG) methods. results tests conducted using PhysioNets Sleep Apnea database suggest excellent thorough comparison multiple models, model selection criteria validation data: Sensitivity (91.93%), Specificity (85.84%), Misclassification (11.94%) Lift (2.7).
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