Individual Time-Dependent Spectral Boundaries for Improved Accuracy in Time-Frequency Analysis of Heart Rate Variability

Abstract

Heart rate variability (HRV) is a major noninvasive technique for evaluating the autonomic nervous system (ANS). Use of time-frequency approach to analyze HRV allows investigating the ANS behavior from the power integrals, as a function of time, in both steady-state and non steady-state. Power integrals are examined mainly in the low-frequency and the high-frequency bands. Traditionally, constant boundaries are chosen to determine the frequency bands of interest. However, these ranges are individual, and can be strongly affected by physiologic conditions (body position, breathing frequency). In order to determine the dynamic boundaries of the frequency bands more accurately, especially during autonomic challenges, we developed an algorithm for the detection of individual time-dependent spectral boundaries (ITSB). The ITSB was tested on recordings from a series of standard autonomic maneuvers with rest periods between them, and the response to stand was compared to the known physiological response. A major advantage of the ITSB is the ability to reliably define the mid-frequency range, which provides the potential to investigate the physiologic importance of this range.

A method for continuously assessing the autonomic response to music-induced emotions through HRV analysis

“…The mean error was low (0.0001 ± 0.0045 Hz, equal to -0.4 ± 2.1%) in agreement with results reported in [2] for simulated signals. In our study, F r (n) was always available, but in those cases where respiratory frequency is not available (neither from respiratory signal nor from the ECG), B HF (n) may be automatically adjusted to the instantaneous spectral properties of the HRV signal [13].…”

Analysis of heart rate variability during exercise stress testing using respiratory information

“…Several approaches to non-stationary analysis of HRV have been proposed in the literature [8], of which time-frequency (TF) analysis is the most common. This approach can be divided into three categories: (i) non-parametric methods based on linear filtering, including the short-time Fourier transform [9][10][11] and the wavelet transform [12][13][14], (ii) non-parametric quadratic TF representations, including the Wigner-Ville distribution and its filtered versions [15][16][17], and (iii) parametric methods based on autoregressive models with time-varying coefficients [18,10]. The smoothed pseudo Wigner-Ville distribution (SPWVD) provides better resolution than non-parametric linear methods, independent control of time and frequency filtering, and power estimates with lower variance than parametric methods when rapid changes occur [16,19].…”

“…In some studies, the HF band is extended to include the whole range of possible respiratory frequencies [27,28,9,29,13], the upper limit being half the mean heart rate (HR). In other cases, the HF band is centered on the respiratory frequency with constant or time-dependent bandwidth [14]. The LF band can be redefined to range from its lower limit (i.e.…”

HRV analysis in local anesthesia using Continuous Wavelet Transform (CWT)

“…It is well documented that the respiratory effect does not necessarily be confounded to the fixed limits (0.15-0.4 Hz) defined for the HF band of the signal [7]. In the literature, different approaches have been proposed for the estimation of the boundaries related to HRV signal components [7]- [11]. All these definitions take only the peak frequency of the respiration signal into account and the boundaries are defined without considering any other aspect (e.g.…”