Spectral analysis of heart rate variability using the integral pulse frequency modulation model

Spectral analysis of heart rate variability (HRV) is a widely accepted approach for assessment of cardiac autonomic function and its relationship to numerous disorders and diseases. As a rule, the non-parametric methods for HRV spectral analysis are tested using the integral pulse frequency modulation (IPFM) model. However, published results with simulated HRV signals show differences requiring further development of the existing methods. With the aim of improving estimation accuracy, an entirely IPFM-based method for HRV analysis is investigated. According to this method, the spectra are computed by finding the least squares solution of two matrix equations that are derived using the IPFM model and involve irregular samples of a signal representing the HRV. The method is validated with various synthesised signals (in all tests, the relative errors of the power estimates at the modulating frequencies are within 3%, and the relative power of the spurious terms is less than 0.8% only) and is furthermore applied to the spectral analysis of R-R interval series obtained from diabetic children. The results, with simulated and real HRV signals, show that the developed method yields very accurate estimations of the spectral region below half the mean heart rate. Moreover, it allows the detection and assessment of certain genuine modulating components beyond the traditional frequency limit of the HRV spectra.