Spectral density

Spectroscopy

Quantum mechanics

Statistics

Autonomic function

Spectral analysis

Computer science

Heart rate

Heart rate variability

Physics

Blood pressure

Mathematics

Autoregressive model

Internal medicine

Artificial intelligence

Medicine

Window function

Wavelet

Frontiers in Neurology

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.

Spectral Analysis of Heart Rate Variability: Time Window Matters