Abstract-Human heartbeat intervals are known to have nonlinear and nonstationary dynamics. In this paper, we propose a model of R-R interval dynamics based on a nonlinear VolterraWiener expansion within a point process framework. Inclusion of second-order nonlinearities into the heartbeat model allows us to estimate instantaneous heart rate (HR) and heart rate variability (HRV) indexes, as well as the dynamic bispectrum characterizing higher order statistics of the nonstationary non-Gaussian time series. The proposed point process probability heartbeat interval model was tested with synthetic simulations and two experimental heartbeat interval datasets. Results show that our model is useful in characterizing and tracking the inherent nonlinearity of heartbeat dynamics. As a feature, the fine temporal resolution allows us to compute instantaneous nonlinearity indexes, thus sidestepping the uneven spacing problem. In comparison to other nonlinear modeling approaches, the point process probability model is useful in revealing nonlinear heartbeat dynamics at a fine timescale and with only short duration recordings. Index Terms-Adaptive filters, approximate entropy (ApEn), heart rate variability (HRV), nonlinearity test, point processes, scaling exponent, Volterra series expansion.
Chen at al. REF use the Volterra Series to model the instantaneous mean of the heartbeat interval (R-R interval) extracted using ECG.
Characterizing Nonlinear Heartbeat Dynamics Within a Point Process Framework
IEEE Transactions on Biomedical Engineering
IEEE Transactions on Biomedical Engineering
Computer Science Medicine