Heart Rate Variability: Measures and Models

TL;DR

This paper evaluates various measures of heart rate variability to classify cardiac health, demonstrating that scale-dependent measures, especially wavelet-based ones, are highly effective even with short records.

Contribution

It introduces a comprehensive comparison of 16 HRV measures, highlights the superiority of scale-dependent methods, and develops a stochastic model of heartbeat dynamics.

Findings

Scale-dependent measures outperform scale-independent ones.

Wavelet transform measures reliably classify heart health with short recordings.

Heartbeat dynamics are better modeled as stochastic processes than chaotic systems.

Abstract

We focus on various measures of the fluctuations of the sequence of intervals between beats of the human heart, and how such fluctuations can be used to assess the presence or likelihood of cardiovascular disease. We examine sixteen such measures and their suitability for correctly classifying heartbeat records of various lengths as normal or revealing the presence of cardiac dysfunction, particularly congestive heart failure. Using receiver-operating-characteristic analysis we demonstrate that scale-dependent measures prove substantially superior to scale-independent ones. The wavelet-transform standard deviation at a scale near 32 heartbeat intervals, and its spectral counterpart near 1/32 cycles/interval, turn out to provide reliable results using heartbeat records just minutes long. We further establish for all subjects that the human heartbeat has an underlying stochastic origin rather than arising from a chaotic attractor. Finally, we develop a mathematical point process that emulates the human heartbeat time series for both normal subjects and heart-failure patients.