A computer implemented method of decomposing a non-stationary signal representative of a physiological phenomenon into a set of self-similar functional components with the QGK (quasi-Gaussian kernel) as a basis element, and for estimating parameters characterizing respective functional components, the method comprising the steps of: applying multiple-pass moving average filters to estimate dynamic and baseline trends in the time course of the signal (step 102), using the trends to extract a guide function indicative of signal fragments over which the functional components are developed, and adaptive segmenting the guide function using zero-crossings as the segmentation points; dividing the non-stationary signal into the regions of component development, the non-stationary signal being segmented at points corresponding to the points of segmentation of the guide function (step 103); compensating for overlap from QGKs matched to preceding functional components to form an ECK (empirical component kernel) (step 104), the recursive sub-steps of: transforming the ECK to a frequency domain using similar basis function algorithm (step 105); estimating the κ (weight) and σ (dispersion) parameters (step 106) and validating the parameter reliability in terms of categorical E, G, S, W and U grades, wherein: E - excellent, G - good, S - satisfactory, W - weak and U - unsuccessful by finding and assessing a best match between the normalized amplitude spectrum of the ECK and the frequency domain template with a Gaussian profile; estimating the onset time τ of QGK with identified κ and σ parameters from the imaginary part of ECK's complex spectrum by finding characteristic points that correspond to defined values of the phase spectrum (step 107); expanding the model of the non-stationary signal composed from the sum of QGKs identified on the preceding cycles of recursion by adding the newly identified QGK (step 108); and re-arranging the identified components after removal of unfavourable QGKs using a categorical parameter ξ for assessment of the identification results (step 109); creating partial models of the non-stationary signal by selection of QGK ensembles the parameters of which belong to predefined classes (step 110). applying multiple-pass moving average filters to estimate dynamic and baseline trends in the time course of the signal (step 102), using the trends to extract a guide function indicative of signal fragments over which the functional components are developed, and adaptive segmenting the guide function using zero-crossings as the segmentation points; dividing the non-stationary signal into the regions of component development, the non-stationary signal being segmented at points corresponding to the points of segmentation of the guide function (step 103); compensating for overlap from QGKs matched to preceding functional components to form an ECK (empirical component kernel) (step 104), the recursive sub-steps of: transforming the ECK to a frequency domain using similar basis function algorithm (step 105); estimating the κ (weight) and σ (dispersion) parameters (step 106) and validating the parameter reliability in terms of categorical E, G, S, W and U grades, wherein: E - excellent, G - good, S - satisfactory, W - weak and U - unsuccessful by finding and assessing a best match between the normalized amplitude spectrum of the ECK and the frequency domain template with a Gaussian profile; estimating the onset time τ of QGK with identified κ and σ parameters from the imaginary part of ECK's complex spectrum by finding characteristic points that correspond to defined values of the phase spectrum (step 107); expanding the model of the non-stationary signal composed from the sum of QGKs identified on the preceding cycles of recursion by adding the newly identified QGK (step 108); and re-arranging the identified components after removal of unfavourable QGKs using a categorical parameter ξ for assessment of the identification results (step 109); creating partial models of the non-stationary signal by selection of QGK ensembles the parameters of which belong to predefined classes (step 110). transforming the ECK to a frequency domain using similar basis function algorithm (step 105); estimating the κ (weight) and σ (dispersion) parameters (step 106) and validating the parameter reliability in terms of categorical E, G, S, W and U grades, wherein: E - excellent, G - good, S - satisfactory, W - weak and U - unsuccessful by finding and assessing a best match between the normalized amplitude spectrum of the ECK and the frequency domain template with a Gaussian profile; estimating the onset time τ of QGK with identified κ and σ parameters from the imaginary part of ECK's complex spectrum by finding characteristic points that correspond to defined values of the phase spectrum (step 107); expanding the model of the non-stationary signal composed from the sum of QGKs identified on the preceding cycles of recursion by adding the newly identified QGK (step 108); and re-arranging the identified components after removal of unfavourable QGKs using a categorical parameter ξ for assessment of the identification results (step 109); creating partial models of the non-stationary signal by selection of QGK ensembles the parameters of which belong to predefined classes (step 110).