Heterogeneous recurrence analysis of heartbeat dynamics for the identification of sleep apnea events

• This work develops new models to delineate heterogeneous recurrence patterns inherent in nonlinear dynamics of cardiac electrical activity.
• This work exploits heterogeneous recurrence variations and links with the disease patterns for the identification of OSA events.
• Our approach outperforms classical recurrence quantitative analysis used in existing approaches for OSA detection.

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.