Compressed sensing technology-based spectral estimation of heart rate variability using the integral pulse frequency modulation model

In this paper, a compressed sensing (CS)-based spectral estimation of heart rate variability (HRV) using the integral pulse frequency modulation (IPFM) model is introduced. Previous research in the literature indicated that the IPFM model is widely accepted as a functional description of the cardiac pacemaker, and thus, very useful in modeling the mechanism by which the autonomic nervous system modulates the heart rate (HR). On the other hand, recently CS becomes an emerging technology that has attracted great attention since it is capable of acquiring and reconstructing signals that are considered sparse or compressible, even when the number of measurements is small. Using 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 unprecedented. Numerical results produced by a real RR database of PhysioNet demonstrated that the proposed approach can robustly provide high-fidelity HRV spectral estimates, even under the situation of a degree of incompleteness in the RR data caused by ectopic or missing beats.