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

Abstract

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.