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.
Highlights
Heart rate variability (HRV) is the physiological phenomenon of variation in heart beats
We need to consider the effects of time window length. Both short-term and long-term HRV analysis using the TRS algorithm showed that fingolimod increased high frequency (HF) in patients with multiple sclerosis, only long-term MTRS analysis showed that the changes of low frequency (LF) and HF were consistent with other related HRV parameters
For outcome prediction in the long-run, reduced short-term LF power during controlled respiration is a strong predictor of sudden death in the patients with chronic heart failure [92], and multiple time and frequency domain parameters obtained from a 2-min ECG recordings could predict end-stage renal disease and chronic kidney disease related hospitalization in the participants of the Atherosclerosis Risk in Communities (ARIC) study [93]
Summary
Heart rate variability (HRV) is the physiological phenomenon of variation in heart beats. Power spectral analysis of HRV is analyzed through fast Fourier transform and autoregressive models, by commercial devices or non-commercial software [8] In most cases, both methods obtain comparable results, but we need to notice their differences. Fast Fourier transform based HRV analysis needs artificial interpolation to satisfy the demand on equal distance, but the interpolation would introduce biases It works on a stable ECG segment of at least 5 min, this restriction on length sometimes limits its application (such as in dynamic processes) [2, 9]. The autoregressive method is a popular tool for spectral analysis of HRV, it does not need interpolation, and the length of data required for analysis is shorter than fast Fourier transform. Since this article focuses on spectral analysis, further discussion of this method is out of the scope of this article
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