Time and frequency domain methods for heart rate variability analysis: a methodological comparison

Abstract

The notion that vagal control of the heart closely parallels a variery of psychophysiological phenomena has become widely accepted; however, until relatively recently there has been no simple or noninvasive method of quantifying it. This study quantitatively evaluates and compares two of the most common methods for measuring respiratory-related heart rate fluctuations: Spectral analysis and the Porges technique of detrended, filtered variance. Low-jrequency power was removed from instantaneous, 4 H z RR interval signals using either a first-order linear (Spectral) or third-order parabolic (Porges) polynomial. The Porges technique resulted in signal amplification at a few specijic frequencies.The frequency range and effect of amplification were functions of the sampling frequency and the frequency content of the signal.

Family stress moderates relations between physiological and behavioral synchrony and child self‐regulation in mother–preschooler dyads

“…MindWare created 4 Hz time series based on the mother's and child's separate IBI series through the use of resampling at fixed intervals with interpolation (Berger, Akselrod, Gordon, & Cohen, ; Berntson, Cacioppo, & Quigley, ). The time series was linearly detrended using the second order polynomial in order to minimize nonstationaries in the data (Litvack, Overlander, Carney, & Saul, ). The Hamming window was used to taper the residual series and the time series underwent either a discrete Fourier transformation (DFT) or a fast Fourier transformation (FFT) through the LabVIEW module (National Instruments, Austin, TX) to determine the spectral distribution.…”

Fast time series detrending with applications to heart rate variability analysis

“…The HRV analysis requires reliable and efficient signal processing techniques of the series of RR-intervals. Both time domain and frequency domain analysis can be used to extract useful clinical information from the ECG measurements and the corresponding RR time series [3,4]. However, the extracted RR time series are non-stationary, and contain trends and artifacts that must be removed before analysis.…”

“…However, the extracted RR time series are non-stationary, and contain trends and artifacts that must be removed before analysis. Especially, the frequency domain analysis is sensitive to the trends embedded in the RR time series [3,4]. These trends have a complex behavior and are caused by external effects, corresponding to smooth and slowly varying processes, which can have both oscillating and stochastic components.…”

Successive RR Interval Analysis of PVC With Sinus Rhythm Using Fractal Dimension, Poincaré Plot and Sample Entropy Method

“…These premature discharges are due to electrical "irritability" of the heart muscle of the ventricles can be caused by heart attacks, electrolyte imbalances, lack of oxygen, or medications. Conventionally used time and frequency domain parameters of HRV [2,3] are not always suitable for analysis because of the non-stationary characteristic of the ECG. The visual analysis of variability of the Poincaré plot [4] and quantification of the unpredictability and complexity of the heart rate using sample entropy [5] is being increase because they can computed from shorter ECG records.…”