Hemodynamic waveform estimation for monitoring of artificial hearts using artificial neural networks

Makoto Yoshizawa, Makoto Kisanuki, Ken ichi Abe, Tomoyuki Yambe, Shin ichi Nitta

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

For monitoring or control of the artificial heart, it is very important to measure hemodynamic parameters such as cardiac output and blood pressure. It is required to obtain these parameters without direct measurement because the direct measurement using sensors positioned inside the body must cause many problems. The present study tried to estimate, without direct measurement, the waveform of cardiac output or blood pressure by means of an artificial neural network (NN) or a linear time series model (ARX model) on the basis of the data obtained from a mock circulatory system driven by an artificial heart. It was revealed that the NN may be superior to the ARX model from the view point of versatility which will be important on an application stage of the total artificial heart.

Original languageEnglish
Pages (from-to)546-555
Number of pages10
JournalBiomedical Engineering - Applications, Basis and Communications
Volume8
Issue number6
Publication statusPublished - 1996 Jan 1

ASJC Scopus subject areas

  • Biophysics
  • Bioengineering
  • Biomedical Engineering

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