title: Heterogeneous recurrence analysis of heartbeat dynamics for the identification of sleep apnea events.
abstract: Obstructive sleep apnea (OSA) is a common sleep disorder that affects 24% of adult men and 9% of adult women. It occurs due to the occlusion of the upper airway during sleep, thereby leading to a decrease of blood oxygen level that triggers arousals and sleep fragmentation. OSA significantly impacts the quality of sleep and it is known to be responsible for a number of health complications, such as high blood pressure and type 2 diabetes. Traditional diagnosis of OSA relies on polysomnography, which is expensive, time-consuming and inaccessible to the general population. Recent advancement of sensing provides an unprecedented opportunity for the screening of OSA events using single-channel electrocardiogram (ECG). However, existing approaches are limited in their ability to characterize nonlinear dynamics underlying ECG signals. As such, hidden patterns of OSA-altered cardiac electrical activity cannot be fully revealed and understood. This paper presents a new heterogeneous recurrence model to characterize the heart rate variability for the identification of OSA. A nonlinear state space is firstly reconstructed from a time series of RR intervals that are extracted from single-channel ECGs. Further, the state space is recursively partitioned into a hierarchical structure of local recurrence regions. A new fractal representation is designed to efficiently characterize state transitions among segmented sub-regions. Statistical measures are then developed to quantify heterogeneous recurrence patterns. In addition, we integrate classification models with heterogeneous recurrence features to differentiate healthy subjects from OSA patients. Experimental results show that the proposed approach captures heterogeneous recurrence patterns in the transformed space and provides an effective tool to detect OSA using one-lead ECG signals.
authors.affiliation: Department of Industrial and Management Systems Engineering, University of South Florida, Tampa, FL 33620, USA.
authors.forename: Changqing
authors.initials: C
authors.lastname: Cheng
authors.affiliation: Harold and Inge Marcus Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park, PA 16802, USA.
authors.forename: Chen
authors.initials: C
authors.lastname: Kan
authors.affiliation: Harold and Inge Marcus Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park, PA 16802, USA. Electronic address: huy25@engr.psu.edu.
authors.forename: Hui
authors.initials: H
authors.lastname: Yang
mesh_terms: D004562:Electrocardiography; D001330:Electronic Data Processing / Q000379:methods*; D005260:Female; D006339:Heart Rate; D006801:Humans; D008297:Male; D012891:Sleep Apnea Syndromes / Q000175:diagnosis / Q000503:physiopathology
keywords: Electrocardiogram; Fractal representation; Heterogeneous recurrence quantification; Nonlinear dynamics; Obstructive sleep apnea