A new algorithm for wavelet-based heart rate variability analysis

Abstract

One of the most promising non-invasive markers of the activity of the autonomic nervous system is Heart Rate Variability (HRV). HRV analysis toolkits often provide spectral analysis techniques using the Fourier transform, which assumes that the heart rate series is stationary. To overcome this issue, the Short Time Fourier Transform is often used (STFT). However, the wavelet transform is thought to be a more suitable tool for analyzing non-stationary signals than the STFT. Given the lack of support for wavelet-based analysis in HRV toolkits, such analysis must be implemented by the researcher. This has made this technique underutilized. This paper presents a new algorithm to perform HRV power spectrum analysis based on the Maximal Overlap Discrete Wavelet Packet Transform (MODWPT). The algorithm calculates the power in any spectral band with a given tolerance for the band's boundaries. The MODWPT decomposition tree is pruned to avoid calculating unnecessary wavelet coefficients, thereby optimizing execution time. The center of energy shift correction is applied to achieve optimum alignment of the wavelet coefficients. This algorithm has been implemented in RHRV, an opensource package for HRV analysis. To the best of our knowledge, RHRV is the first HRV toolkit with support for wavelet-based spectral analysis.

Resting high frequency heart rate variability is not associated with the recognition of emotional facial expressions in healthy human adults

“…The LF (0.04-0.15 Hz) and HF (0.15-0.4 Hz) components were extracted using an east asymmetric Daubechies wavelets with a length of 8 samples. Maximum error allowed was set as 0.01 (García, Otero, Vila, & Márquez, 2013).…”

Electrical transient modeling for appliance characterization

“…Indeed, because of the half-wave symmetry found in electrical signals (i.e., for a periodic signal g(t) of period P, a half-wave symmetry is characterized by g(t + P/2) = −g(t)), the sinusoid frequencies f i are odd-order harmonics of the fundamental frequency. Note that the nominal value (50 Hz) of the network fundamental frequency is a priori known, but due to its fluctuations around this value over time (i.e., f 0 = 50 + δf ), we have observed that for a correct modeling, f 0 should be considered as unknown and hence one needs to re-estimate the fundamental frequency value for each transient signal 1 .…”

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Multilead ECG data compression using SVD in multiresolution domain

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