Automatic detection and classification of abnormalities for artificial hearts using a hierarchical self-organizing map.

Abstract

A hierarchical self-organizing map (SOM) has been developed for automatic detection and classification of abnormalities for artificial hearts. The hierarchical SOM has been applied to the monitoring and analysis of an aortic pressure (AoP) signal measured from an adult goat equipped with a total artificial heart. The architecture of the network actually consists of 2 different SOMs. The first SOM clusters the AoP beat patterns in an unsupervised way. Afterward, the outputs of the first SOM combined 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. Some experimental results revealed 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.