A new vector space theory-based approach for the time-varying threshold integral pulse frequency modulation model can discriminate between autonomic nervous modulation and stretch-induced effects.
A novel vector space theory-based method for the TVTIPFM model enables better extraction of physiological information, such as differentiating autonomic and stretch effects on SA node R-R intervals.
Several methods have been proposed so far for the analysis of the integral pulse frequency modulation (IPFM) model and detecting its corresponding physiological information. Most of these methods rely on the low-pass filtering method to extract the modulating signal of the model. In this paper, we present an entirely new approach based on vector space theory. The new method is developed for a more comprehensive form of the IPFM model, namely the time-varying threshold integral pulse frequency modulation (TVTIPFM) model. The new method decomposes the driving signals of the TVTIPFM model into a series of orthogonal basis functions and constructs a matrix identity through which the input signals can be obtained by a parametric solution. As a particular case, we apply this method to R-R intervals of the SA node to discriminate between its autonomic nervous modulation and the stretch induced effect.
Seydnejad et al. (Mon,) reported a other. Vector space theory-based approach for TVTIPFM model was evaluated on Discrimination between autonomic nervous modulation and stretch induced effect in R-R intervals. A new vector space theory-based approach for the time-varying threshold integral pulse frequency modulation model can discriminate between autonomic nervous modulation and stretch-induced effects.