Chinese Journal of Tissue Engineering Research ›› 2011, Vol. 15 ›› Issue (9): 1631-1634.doi: 10.3969/j.issn.1673-8225.2011.09.027

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Effects of rotary speed and pressure changes of blood pump on flow rate base on two cascaded neural networks

Xuan Yan-jiao, Chang Yu   

  1. College of Life Science and Bio-engineering, Beijing University of Technology, Beijing  100124, China
  • Received:2010-10-13 Revised:2010-11-14 Online:2011-02-26 Published:2011-02-26
  • Contact: Chang Yu, Doctor, Associate professor, College of Life Science and Bio-engineering, Beijing University of Technology, Beijing 100124, China changyu@bjut.edu. cn
  • About author:Xuan Yan-jiao, College of Life Science and Bio-engineering, Beijing University of Technology, Beijing 100124, China xuanyanjiao@126. com
  • Supported by:

    the Innovation Team Program of Talent Training Plan of Beijing University of Technology, No. 31500054R5001*; 211 Project-Talent Training Plan of Beijing University of Technology, No. 01500054R8001*; the National Natural Science Foundation of China, No. 10972016*, 31070754*, 10872013*, 11072012*

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Abstract:

BACKGROUND: In clinical applications, the timely and accurate detection of the output flow of artificial heart is very necessary, as it is directly related to the effects of zoopery and clinical application. However, it is difficult to achieve.
OBJECTIVE: To explore how cardiovascular hemodynamic parameters reflect the blood pump’s working status during the process of assist circulation and obtain the neural network results which grasp the characteristics of the output flow of blood pump by experiments and theoretical analysis.
METHODS: In order to estimate and test the working condition of the blood pump in ventricular assist device correctly, two types of neural networks were established, and the effects of changing rotary speed and pressure on flow rate of the pump were estimated. In the first order, the flow rate affected by continuously changing blood pressure at different rotational speed was estimated by the BP neural network. In the second order, radial basis function neural network was applied to estimate the flow rate of the pump at different rotational speed of the blood pump.
RESULTS AND CONCLUSION: This method showed a better estimation ability to estimate the flow rate according to?different rotational speed and blood pressure compared with previous methods.

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