Key result
Multiple basis function sets improve blood pressure tracking over single sets or recursive least squares.
An algorithm using multiple sets of basis functions improves the tracking capability of time-varying dynamic systems, such as blood pressure data, compared to single-set methods.
May enhance dynamic blood pressure modeling; extends multi-basis approaches but leaves clinical translation open pending validation.
We extend a recently developed algorithm that expands the time-varying parameters onto a single set of basis functions, to multiple sets of basis functions. This feature allows the capability to capture many different dynamics that may be inherent in the system. A single set of basis functions that has its own unique characteristics can best capture dynamics of the system that have similar features. Therefore, for systems that have multiple dynamics, the use of a single set of basis functions may not be adequate. Computer simulation examples do indeed show the benefit of using multiple sets of basis functions over the single set of basis functions for cases with many switching dynamics. Moreover, the proposed method remains accurate even under significant noise contamination. Application of the proposed approach to blood pressure data likewise indicate better tracking capability of the two sets of basis function than the recursive least squares or a single set of basis functions.
No takes yet. Share an insight, caveat, or question.
Chon et al. (2005) studied this question. Multiple sets of basis functions vs. Single set of basis functions or recursive least squares was evaluated on Tracking capability and accuracy. Multiple sets of basis functions provided better tracking capability for systems with multiple dynamics, including blood pressure data, compared to a single set or recursive least squares.
Synapse has enriched 4 closely related papers on similar clinical questions. Consider them for comparative context: