Modeling of a rotary blood pump

Nestler, Frank, Bradley, Andrew P., Wilson, Stephen J. and Timms, Daniel L. (2014) Modeling of a rotary blood pump. Artificial Organs, 38 3: 182-190. doi:10.1111/aor.12142


Author Nestler, Frank
Bradley, Andrew P.
Wilson, Stephen J.
Timms, Daniel L.
Title Modeling of a rotary blood pump
Journal name Artificial Organs   Check publisher's open access policy
ISSN 0160-564X
1525-1594
Publication date 2014-01-01
Year available 2013
Sub-type Article (original research)
DOI 10.1111/aor.12142
Volume 38
Issue 3
Start page 182
End page 190
Total pages 9
Place of publication Hoboken, NJ, United States
Publisher Wiley-Blackwell
Language eng
Abstract The accurate representation of rotary blood pumps in a numerical environment is important for meaningful investigation of pump-cardiovascular system interactions. Although numerous models for ventricular assist devices (VADs) have been developed, modeling methods for rotary total artificial hearts (rTAHs) are still required. Therefore, an rTAH prototype was characterized in a steady flow, hydraulic test bench over a wide operational range for pump and hydraulic parameters. In order to develop a generic modeling method, a data-driven modeling approach was chosen. k-Nearest-neighbors, artificial neural networks, and support vector machines (SVMs) were the machine learning approaches evaluated. The best performing parameters for each algorithm were determined via optimization. The resulting multiple-input-multiple-output models were subsequently assessed under identical conditions, and a SVM with a radial basis function kernel was identified as the best performing. The achieved root mean squared errors were 0.03L/min, 0.06L/min, and 0.18W for left and right flow and motor power consumption, respectively. In comparison with existing models for VADs, the flow errors are more than 70% lower. Further advantages of the SVM model are the robustness to measurement noise and the capability to operate outside of the trained parameter range. This proposed modeling method will accelerate further device refinements by providing a more appropriate numerical environment in which to evaluate the pump-cardiovascular system interaction.
Keyword Data-driven model
Machine learning
Support vector machine
Total artificial heart
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2014 Collection
School of Information Technology and Electrical Engineering Publications
 
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