Applying hierarchical fuzzy automatons to automatic diagnosis

Hierarchical fuzzy automatons (HFAs) are employed to perform automatic diagnosis on a signal represented as set of discrete time measurements. The HFA incorporates two levels of hierarchy with the lower level identifying structures within the signal and the top level integrating the results from lower level automatons. An adaptive resonance theory (ART) artificial neural network (ANN) is used to determine input tokens and to tokenize the input. The tokens generated by the ANN are given fuzzy memberships using information derived from the state of the ANN. In addition, a general methodology is presented for constructing HFAs. HFAs are applied to the problem of determining whether an ECG recording is normal or shows atrial fibrillation.