A comparison of two Hilbert spectral analyses of heart rate variability

Abstract

The present paper compares the performance of two Hilbert spectral analyses when applied to a synthetic RR series from a nonstationary integral pulse frequency modulation model and to real RR series from a dataset of normal sinus arrhythmia. The Hilbert-Huang transformation based on empirical mode decomposition is compared to the presently introduced Hilbert-Olhede-Walden transformation based on stationary wavelet packet decomposition. The comparison gives consistent results pointing to a superior performance of the Hilbert-Olhede-Walden transformation showing 33-163 times smaller deviations when estimating the instantaneous frequency traces of the synthetic RR series. Artificial fluctuations caused by mode mixing in the Hilbert-Huang spectrum are seen in both the synthetic and real RR series. It can be concluded that the instantaneous frequencies and amplitudes estimated by the Hilbert-Huang transformation should be interpreted with caution when investigating heart rate variability.

Interaction-dominant dynamics in human cognition: Beyond 1/ƒα fluctuation.

“…First, there are other transformations besides wavelets that decompose a response series into the time-scale plane of Figure 2 in the main text. There are, for example, the short-time Fourier transformation (Gabor, 1946); the Hilbert transform combined with either Fourier bandpass decomposition (Bloomfield, 1976), empirical mode decomposition (Huang et al, 1998), or wavelet packet decomposition (Ihlen, 2009; Olhede & Walden, 2005); and the Wigner-Ville transform (Wigner, 1932), to name but a few. The wavelet approach has been chosen in the present article because it is the most utilized transformation in the multifractal analysis of multiplicative cascading processes.…”

Discrimination power of long-term heart rate variability measures for chronic heart failure detection

“…The frequency-domain HRV measures rely on the estimation of power spectral density (PSD). Several methods were proposed in literature in order to estimate PSD of RR intervals [12,23,26,30]. In this study, we estimated PSD both by Welch's averaged modified periodogram [35] and by Lomb-Scamble periodogram [22].…”

A novel online method to monitor autonomic nervous activity based on arterial wall impedance and heart rate variability

“…The variation of beat-to-beat intervals, referred to as heart rate variability (HRV), can usually be calculated by analyzing the time series of R-R intervals from ECG. Various HRV spectral analyses have been proposed using the Fast Fourier Transform (FFT), the maximum entropy method (MEM) and Wavelet Transform (WT) analysis [1,14,16,22,26,30,32,36]. The principle of spectral analysis is based on the fact that sympathetic nervous system (SNS) and parasympathetic nervous system (PNS) activity affects heart rate (R-R interval) variability in specific frequency bands [6].…”

“…It is known that one part of the ANS based on heart activity is reflected in fluctuations of the heart rate [6]. Many studies to date on heart rate variability (HRV) have been proposed using frequency domain analysis and time domain analysis [1,14,16,22,26,30,32,36], because HRV is an interesting and reliable quantitative approach to physiological analysis as well as a widely used clinical tool [14]. Recent studies have also used power spectral analysis to evaluate ANS activity from changes in blood pressure and heart rate during physical evaluation [33,37] or when a patient is under anaesthesia [9,12,13,15,20].…”