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December 22, 2005IEEE Transactions on Biomedical Engineering

Analysis of Heartbeat Dynamics by Point Process Adaptive Filtering

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Population

Simulated heartbeat data and actual heartbeat data from a tilt table experiment, as well as from healthy…

Design

Other

Authors

RBRiccardo BarbieriUniversity of PisaEBE. N. BrownThe University of Texas MD Anderson Cancer Center

Discussion

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Implication

May enable refined dynamic HRV metrics in research; leaves open clinical validation and adoption.

Structured PICO

P
Population
Simulated heartbeat data and actual heartbeat data from a tilt table experiment, as well as from healthy subjects and subjects with congestive heart failure during sleep
I
Intervention
Point process adaptive filter algorithm based on a history dependent inverse Gaussian model
O
Outcome
Estimation of time-varying parameters and computation of new measures of heart rate variability

A novel point process adaptive filter algorithm provides a new mathematical approach for characterizing heartbeat dynamics and computing heart rate variability.

Cite This Study

Barbieri et al. (2005) studied this question.

synapsesocial.com/papers/6a71df5d6ceb2bbd16e03fb9https://doi.org/10.1109/tbme.2005.859779

Topics

Heart failureHFrEF treatment
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Multivariate time-variant identification of cardiovascular variability signals: a beat-to-beat spectral parameter estimation in vasovagal syncope1997 · 46 citations
  2. 2Pole-tracking algorithms for the extraction of time-variant heart rate variability spectral parameters1995 · 77 citations
  3. 3Coarse-graining spectral analysis: new method for studying heart rate variability1991 · 356 citations
  4. 4Time-variant power spectrum analysis for the detection of transient episodes in HRV signal1993 · 222 citations
  5. 5Influence of respiration on heart rate and blood pressure fluctuations1993 · 237 citations