An automatic monitoring system for artificial hearts using a hierarchical self-organizing map

Xian Zheng Wang, Makoto Yoshizawa, Akira Tanaka, Ken Ichi Abe, Kou Imachi, Tomoyuki Yambe, Shin Ichi Nitta

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


A hierarchical self-organizing map (SOM) was developed for automatic detection and classification of abnormalities of artificial hearts. The hierarchical SOM was applied to the monitoring and analysis of the aortic pressure (AoP) signal measured from an adult goat equipped with a total artificial heart. The architecture of the network consists of two different SOMs. The first SOM clusters the AoP beat patterns in an unsupervised way. Afterwards, the outputs of the first SOM combining with the original time-domain features of beat-to-beat data are fed to the second SOM for final classification. Each input vector of the second SOM is associated with a class vector. This class vector is assigned to every node in the second map as an output weight and learned according to Kohonen's learning rule. Experimental results showed that a certain abnormality caused by breakage of sensors could be identified and detected correctly, and that the change in the state of the circulatory system could be recognized and predicted to some extent.

Original languageEnglish
Pages (from-to)198-204
Number of pages7
JournalJournal of Artificial Organs
Issue number3
Publication statusPublished - 2001 Jan 1


  • Aortic pressure
  • Artificial heart
  • Artificial neural network
  • Self-organizing map
  • Supervised learning
  • Unsupervised learning

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Biomaterials
  • Biomedical Engineering
  • Cardiology and Cardiovascular Medicine


Dive into the research topics of 'An automatic monitoring system for artificial hearts using a hierarchical self-organizing map'. Together they form a unique fingerprint.

Cite this