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Related Experiment Video

Updated: Jun 16, 2026

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

Estimation of three- and four-element windkessel parameters using subspace model identification.

Taco Kind¹, Theo J C Faes, Jan-Willem Lankhaar

¹Department of Pulmonology, VU University Medical Center, Amsterdam 1007 MB, The Netherlands. t.kind@vumc.nl

IEEE Transactions on Bio-Medical Engineering

|February 23, 2010

Summary

Subspace model identification (SMI) robustly estimates parameters for the three-element windkessel model. However, SMI shows systematic errors for the four-element model due to arterial inertance estimation issues.

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Last Updated: Jun 16, 2026

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

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Area of Science:

Biomedical Engineering
Physiology
Computational Biology

Background:

The Windkessel model is a standard for characterizing vascular dynamics.
Accurate parameter estimation is crucial for understanding cardiovascular function.

Purpose of the Study:

To evaluate a noniterative subspace model identification (SMI) algorithm for Windkessel model parameter estimation.
To compare SMI with existing iterative methods using simulated systemic circulation data.

Main Methods:

Employed a noniterative subspace model identification (SMI) algorithm.
Applied physical foreknowledge to estimate parameters in three- and four-element Windkessel models.
Utilized simulated systemic circulation data to assess parameter estimation errors.

Main Results:

SMI demonstrated no significant systematic errors for the three-element Windkessel model.
SMI showed superior robustness against random errors compared to other methods (P < 0.05 at 18 dB SNR).
A significant systematic error in arterial inertance (L) estimation was observed for the four-element model using SMI.

Conclusions:

SMI effectively derives physical parameters using physiological knowledge, particularly for the three-element Windkessel model.
The four-element Windkessel model's application is questionable due to low identifiability of arterial inertance (L) with SMI.
Increased noise levels led to outliers in parameter estimates, primarily affecting arterial inertance (L).