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Interbeat Interval Filtering
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AI
DOI：10.1109/LSP.2024.3522853.png)
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Abstract
En 中文
Several inhibitory and excitatory factors regulate the beating of the heart. Consequently, the interbeat intervals (IBIs) vary around a mean value. Various statistics have been proposed to capture heart rate variability (HRV) to give a glimpse into this balance. However, these statistics require accurate estimation of IBIs as a first step, which can be challenging especially for signals recorded in ambulatory conditions. We propose a lightweight state-space filter that models the IBIs as samples of an inverse Gaussian distribution with time-varying parameters. We make the filter robust against outliers by adapting the probabilistic data association filter to the setup. We demonstrate that the resulting filter can accurately identify outliers and the parameters of the tracked distribution can be used to compute a specific HRV statistic (standard deviation of normal-to-normal intervals, SDNN) without further analysis.
Keywords:
Heart rate variability
Filtering algorithms
Gaussian distribution
Adaptive filters
Standards
Random variables
Heart beat
Computational modeling
Adaptation models
Accuracy
Interbeat interval
heart rate variability
state-space
robust filter
probabilistic data association
Journal
IF:
9.6
Papers:
1.1W
Citations:
1.7W
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