Research and Development of a Novel Algorithm for Heart Rate Variability Spectral Estimation

Spectral analysis of Heart Rate Variability (HRV) may provide an insight into the underlying autonomic control activities. Since the Integral Pulse Frequency Modulation (IPFM) model has been widely known as a functional description of the cardiac pacemaker, it is adopted to model the mechanism by which the Autonomic Nervous System (ANS) modulates the Heart Rate (HR). On the other hand, in the past few years an alternative sampling or sensing theory, referred to as the Compressive Sampling or Compressed Sensing (CS), enables the faithful recovery of certain signals and images from far fewer samples or measurements than traditional methods use. In fact, using the IPFM model, we have developed a novel CS-based algorithm for deriving the amplitude spectrum of the modulating signal for HRV assessments in a previous research. It is worth noting that the application of CS theory into the HRV spectral estimation is unprecedented. In this project, we further explore and improve the previously developed CS-based HRV spectral estimation method by aiming to (1) seek for intensive investigations and modifications on the CS-based HRV algorithm itself, and (2) evaluate the performances of a variety of novel CS algorithms to investigate their feasibility and applicability into the HRV spectral estimation using the standard RR databases drawn from the PhysioNet, towards the optimal algorithmic design of the CS-based HRV spectral estimation.

Project ID:PB10507-2954
External Project ID:MOST105-2221-E182-003