In 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 they have not been thoroughly assessed in order to select the best for a respiration rate estimation from a volumetric surrogate signal. This paper discusses the implementation of some algorithms for instantaneous frequency estimation in MATLAB and compares their performance from known synthetic signals resembling real-world respiratory signals, by using goodness of fit parameters. In detail, a method based on the first conditional spectral moment of the time-frequency distribution of the input signal x, another using the derivative of the phase of the analytic signal of x (found using the Hilbert transform), and others based on second-order auto-regressive models. Goodness of fit (maximum absolute error and mean-squared error) between the estimated and the expected ideal instantaneous frequencies were computed. The root MUSIC algorithm outperforms the others under assessment, showing its superiority for instantaneous respiratory frequency estimation from a volumetric surrogate signal.