Spectral Analysis of Heart Rate Variability: Time Window Matters

Kai Li; Heinz Rüdiger; Tjalf Ziemssen

doi:10.3389/fneur.2019.00545

Spectral Analysis of Heart Rate Variability: Time Window Matters

Front Neurol. 2019 May 29:10:545. doi: 10.3389/fneur.2019.00545. eCollection 2019.

Authors

Kai Li^{1

2}, Heinz Rüdiger¹, Tjalf Ziemssen^{1

3}

Affiliations

¹ Autonomic and Neuroendocrinological Lab, Center of Clinical Neuroscience, University Hospital Carl Gustav Carus, Dresden University of Technology, Dresden, Germany.
² Department of Neurology, Beijing Hospital, National Center of Gerontology, Beijing, China.
³ Department of Neurology, University Hospital Carl Gustav Carus, Dresden University of Technology, Dresden, Germany.

Abstract

Spectral analysis of heart rate variability (HRV) is a valuable tool for the assessment of cardiovascular autonomic function. Fast Fourier transform and autoregressive based spectral analysis are two most commonly used approaches for HRV analysis, while new techniques such as trigonometric regressive spectral (TRS) and wavelet transform have been developed. Short-term (on ECG of several minutes) and long-term (typically on ECG of 1-24 h) HRV analyses have different advantages and disadvantages. This article reviews the characteristics of spectral HRV studies using different lengths of time windows. Short-term HRV analysis is a convenient method for the estimation of autonomic status, and can track dynamic changes of cardiac autonomic function within minutes. Long-term HRV analysis is a stable tool for assessing autonomic function, describe the autonomic function change over hours or even longer time spans, and can reliably predict prognosis. The choice of appropriate time window is essential for research of autonomic function using spectral HRV analysis.

Keywords: fast fourier tranform (FFT); heart rate variability; long-term; multiple trigonometric regressive spectral analysis; short-term; trigonometric regressive spectral analysis.