Analysis Of Heartbeat Dynamics Research Articles

Integral pulse frequency modulation model driven by sympathovagal dynamics: Synthetic vs. real heart rate variability

Computational models that generate synthetic heart rate variability (HRV) series constitute important tools for the assessment of the effect of autonomic nervous system activity on cardiovascular control, and for the evaluation of novel algorithms using synthetic data. A widely used technique for synthetic HRV generation is the integral pulse frequency modulation (IPFM) model; however, IPFM relies on the HRV spectral paradigm, which cannot separate sympathetic and vagal oscillations that are overlapped in the low-frequency band (0.04–0.15 Hz). To overcome this limitation, a novel IPFM-inspired model driven by cardiac sympathetic and vagal dynamics estimated from HRV is proposed, where our recently developed sympathetic and parasympathetic activity indices that rely on orthonormal Laguerre expansions of the RR interval autoregressive kernels are exploited. The performance of the proposed model is evaluated by comparing the synthetic vs. real RR interval series in a simulation study involving postural changes, with real HRV data gathered from 10 healthy subjects. Moreover, the performance of the proposed model is compared with that of the standard IPFM to discern different autonomic control states associated with resting and postural changes. The results confirm that the proposed physiologically inspired model adequately predicts RR intervals during resting and postural changes. The proposed model clearly outperforms the standard IPFM method, considering both median error, and maximum error. The developed model provides valuable insights for a better understanding of the sympathovagal activity in the analysis of heartbeat dynamics.

Analysis of Heartbeat Dynamics by Point Process Adaptive Filtering

Heartbeats are a point process yet, most of the current analysis methods do not model this important characteristic of these data. We describe human heartbeat time series as a history dependent inverse Gaussian model. We present a point process adaptive filter algorithm to estimate the model's time-varying parameters, and use it to compute new measures of heart rate variability. We apply our algorithm to analyze simulated heartbeat data and actual heartbeat data from a tilt table experiment and from healthy subjects and subjects with congestive heart failure during sleep. Our results suggest a new approach for characterizing heartbeat dynamics.