Algorithms to estimate the instantaneous-frequency of a respiratory time-varying sequence

Alberto Taboada-Crispi, Lizmary Rivera, Maikol Barber Pérez


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.

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La Universidad de las Ciencias Informáticas (UCI), a través del sello editorial Ediciones Futuro, publica los contenidos de la Revista Cubana de Ciencias Informáticas (RCCI) bajo licencia Creative Commons de tipo Atribución 4.0 Internacional (CC BY 4.0). Esta licencia permite a otros distribuir, mezclar, ajustar y construir a partir de su obra, incluso con fines comerciales, siempre que le sea reconocida la autoría de la creación original.