Resumen

TABOADA-CRISPI, Alberto; RIVERA, Lizmary  y  BARBER PEREZ, Maikol. Algorithms to estimate the instantaneous-frequency of a respiratory time-varying sequence. Rev cuba cienc informat [online]. 2020, vol.14, n.4, pp. 102-122.  Epub 01-Dic-2020. ISSN 2227-1899.

On various occasions, algorithms to estimate instantaneous-frequency from a cyclic (seasonal) sequence to detect slow changes are needed. That is the case of the estimation of the variations of the respiratory rate for diagnostic purposes. There are a few possible procedures to estimate such an instantaneous-frequency, but without a thorough assessment to compute the respiration rate from a volumetric surrogate signal. This paper discusses the implementation of some algorithms for instantaneous-frequency estimation in MATLAB, comparing their performance from known synthetic signals, which resemble real-world respiratory signals, by using the goodness of fit parameters. We used a method based on the first conditional spectral moment of the time-frequency distribution of the input signal x, and other using the derivative of the phase of the analytic signal of x (found using the Hilbert transform). We also used methods based on second-order auto-regressive models. We computed the goodness of fit (maximum absolute and mean-squared errors) between the estimated and the expected ideal instantaneous-frequencies. The root MUSIC algorithm outperforms the others under assessment, showing its superiority for instantaneous-respiratory frequency estimation from a volumetric surrogate signal.

Palabras clave : Respiratory signals; Instantaneous-frequency estimation; Time-frequency distributions; Hilbert transform; Auto-regressive models; root MUSIC.

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