Compressed sensing for integral pulse frequency modulation (IPFM)-based heart rate variability spectral estimation

In this paper, a Compressed Sensing (CS) based spectral analysis of Heart Rate Variability (HRV) using the Integral Pulse Frequency Modulation (IPFM) model is introduced. Previous research in literature indicated that the IPFM model is considered as a functional description of the cardiac pacemaker and thus is very useful in modeling the mechanism by which the Autonomic Nervous System (ANS) modulates the Heart Rate (HR). On the other hand, in recent years CS has attracted great attention over many aspects of signal processing applications. According to the IPFM model, we here present a CS-based algorithm for deriving the amplitude spectrum of the modulating signal for HRV assessments. In fact, the application of the CS method into HRV spectral estimation is novel and unprecedented in HRV analysis. Numerical experimental results demonstrated that the proposed approach can robustly yield accurate HRV spectral estimates, even under the situation of a degree of incompleteness in the interbeat interval or RR data caused by ectopic or missing beats.