A syntactic methodology for automatic diagnosis by analysis of continuous time measurements using hierarchical signal representations

In this paper, we present a methodology for automatic diagnosis of systems characterized by continuous signals. For each condition considered, the methodology requires the development of an alphabet of signal primitives, and a set of hierarchical fuzzy automatons (HFAs). Each alphabet is adaptively obtained by training an adaptive resonance theory (ART2) architecture with signal segments from a particular condition. Then, the original signal is transformed into a string of vectors of primitives, where each vector of primitives replaces a signal segment in the original signal. The string, in turn, is presented to the HFA characterizing that particular condition. Each set of HFA consists of a main automaton identifying the entire signal, and several sub-automata each identifying a particular significant structure in the signal. A transition in the main automaton occurs (i.e., the main automaton moves from one state to another) if the corresponding subautomaton recognizes a token where a token is a portion of the string of vectors of signal primitives with a significant structure. The fuzziness in automaton operation adds flexibility to the operation of the automaton, enabling the processing of imperfect input, allowing for toleration measurement noise and other ambiguities. The methodology is applied to the problem of automatic electrocardiogram diagnosis.