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Analysis of Heartbeat Dynamics by Point Process Adaptive Filtering

2005; Institute of Electrical and Electronics Engineers; Volume: 53; Issue: 1 Linguagem: Inglês

10.1109/tbme.2005.859779

1558-2531

Riccardo Barbieri, E. N. Brown,

Blood Pressure and Hypertension Studies

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.