Spectral decomposition of heart rate variability using generalized harmonic analysis. (September 2021)

• Record Type: Journal Article
• Authors: Satoh, Noriaki
• Is Part Of: Biomedical signal processing and control. Volume 70(2021)
• Publication Date: 2021-09
• Subjects: Autoregressive model -- Generalized harmonic analysis -- Heart rate variability -- Integral pulse frequency modulation model -- Lomb-Scargle periodogram -- Pisarenko harmonic decomposition -- Spectral decomposition; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.103050
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Generalized harmonic analysis is used for decomposing harmonics of heart rate signal. Compared with autoregressive model or Pisarenko harmonic decomposition. Useful tool for calculating the power of a component. The limitations of generalized harmonic analysis are still discussed. Abstract: In this paper, a method for decomposing harmonics in the spectrum of heart rate variability (HRV) using generalized harmonic analysis (GHA) is introduced. First, a simulated RR interval signal generated by an integral pulse frequency modulation model was decomposed spectra by GHA, Pisarenko harmonic decomposition (PHD), and autoregressive (AR) spectral decomposition model. The spectral profiles were obtained by the GHA, PHD, and AR methods for various numbers of extracted sinusoids and model orders from 1 to 48. The spectral profiles of GHA were the most stable. Of the power values of the sinusoids extracted by each method, it was clear that the power values estimated by GHA were approximately equal to the mean square value and closer than that obtained using the PHD or a fast Fourier transform (FFT). Second, a comparison of the power of the low-frequency (LF) and high-frequency (HF) band components reveals that the values obtained by GHA are similar to those obtained by FFT for the analysis using a real ECG signal. Third, Bland-Altman analysis reveals that LF and HF band power value calculated by the GHA are compatible with ones by the Lomb-Scargle periodogram using MIT-BIH normal sinus rhythm database from short term recordings of 30 min. These results suggest that GHA is a useful tool for calculating the power of a component in the spectral analysis of HRV. The limitations of spectral decomposition by GHA are still discussed.