Spectral density estimation

About: Spectral density estimation is a research topic. Over the lifetime, 5391 publications have been published within this topic receiving 123105 citations.

Effects of RR segment duration on HRV spectrum estimation.

Abstract: Although patterns of heart rate variability (HRV) hold considerable promise for clarifying issues in clinical applications, the inappropriate quantification and interpretation of these patterns may obscure critical issues or relationships and may impede rather than foster the development of clinical applications. The duration of the RR interval series is not a matter of convenience but a fine balance between two important issues: acceptable variance and stationarity of the time series on one hand, and acceptable resolution of the spectral estimate and reduced spectral leakage on the other. Further, in the standard short-term HRV analysis, it has been observed that the previous studies in HRV spectral analysis use a wide range of RR interval segment duration for spectral estimation by Welch's algorithm. The standardization of RR interval segment duration is also important for comparisons among studies and is essential for within-study experimental contrasts. In the present study, a comparative analysis for RR interval segment durations has been made to propose an optimal RR interval segment duration. Firstly a simulated signal was analyzed with Hann window and zero padding for the segment lengths of 1024, 512, 256 and 128 samples resampled at 4 Hz with 50% overlapping. Again, the above procedure was applied to RR interval series and it was concluded that segment length of 256 samples with 50% overlapping provides a smoothed spectral estimate with clearly outlined peaks in low- and high-frequency bands. This easily understandable and interpretable spectral estimate leads to a better visual and automated analysis, which is not only desirable in basic physiology studies, but also a prerequisite for a widespread utilization of frequency domain techniques in clinical studies, where simplicity and effectiveness of information are of primary importance.

An Adaptive Time-Efficient Technique for Harmonic Estimation of Nonstationary Signals

Abstract: Model-based parametric techniques offer many advantages over conventional discrete Fourier transform-based methods for harmonic/interharmonic estimation. However, high computational requirements restrict their applications to offline analysis purpose. In this paper, an adaptive technique based on estimation of signal parameters via rotational invariance technique (ESPRIT) is proposed that optimizes the accuracy and computation time for harmonic/interharmonic estimation of stationary as well as nonstationary power supply signals. This method first estimates the order of the model (number of sinusoids present in the distorted power supply signal) and then adjusts the autocorrelation matrix dimension based on reconstruction error. The performance of the proposed method is validated on the time-varying simulated signal, measured synthetic signal, and actual voltage signal of a distribution system supplying electric arc welding load. The comparison of the results with the short-time Fourier transform and the sliding window ESPRIT techniques shows that the proposed approach considerably reduces the computational time of high-resolution ESPRIT method along with better accuracy.

Power-spectrum identification in terms of rational models

Abstract: A technique is described for the identification of unknown power-spectral densities from sampled data in terms of a rational function of z The problem is reduced to the minimization of a function of K parameters, where K is the order of the numerator of the model This criterion, called the "minimum residual" criterion, reduces to the maximum likelihood criterion when the observed signal is Gaussian A computational technique is described for minimizing this function which uses filtering and correlation to obtain the gradient and an iterative descent method due to M J D Powell for minimization Some computational results are given in which the method is compared with all-pole and conventional spectrum estimation techniques