Interbeat Interval Filtering

TL;DR

This paper introduces a lightweight, robust state-space filter for accurately estimating inter-beat intervals and heart rate variability from ambulatory signals, even in the presence of outliers.

Contribution

It proposes a novel inverse Gaussian-based state-space filter with outlier robustness, enabling direct HRV statistic computation from IBIs.

Findings

Accurately identifies outliers in IBI data

Provides reliable HRV estimates without additional processing

Effective in ambulatory conditions

Abstract

A number of inhibitory and excitatory factors regulate the beating of the heart. Consequently, the inter-beat intervals (IBIs) are not constant but vary around a mean value, even in the absence of external factors like exercise or stress. Various statistics have been proposed to capture the heart rate variability (HRV) to provide a glimpse into this balance. These statistics usually require accurate estimation of IBIs as a first step. However, estimating IBIs accurately can be challenging in practice, 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) without further analysis.