This paper presents a complementary study of the methodology for diagnosing of pathologies, based on relevance analysis of stochastic (time-variant) features that are extracted from t-f representations of biosignal recordings. Dimension reduction is carried out by adapting in time commonly used latent variable techniques for a given relevance function, as evaluation measure of time-variant transformation. Examples of both unsupervised and supervised training are deliberated for distinguishing the set of most relevant stochastic features. Besides, two different combining approaches for feature selection are studied. Firstly, when the considered input set comprises a single type of stochastic features, that is, having the same principle of generation. Secondly, when the whole input set of parameters is taken into consideration no matter of their physical meaning. For validation purposes, the methodology is tested for the concrete case of diagnosing of obstructive sleep apnea. Achieved results related to performed accuracy and dimension reduction are comparable with respect to other outcomes reported in the literature, and thus clearly showing that proposed methodology can be focused on finding alternative methods minimizing the parameters used for pathology diagnosing.