Title :
Applying hierarchical fuzzy automatons to automatic diagnosis
Author :
Tümer, M. Borahan ; Belfore, Lee A., II ; Ropella, Kristina M.
Author_Institution :
Dept. of Electr. & Comput. Eng., Marquette Univ., Milwaukee, WI, USA
Abstract :
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
Keywords :
ART neural nets; automata theory; electrocardiography; fuzzy neural nets; fuzzy set theory; medical diagnostic computing; medical signal processing; signal representation; ECG recording; HFAs; adaptive resonance theory artificial neural network; atrial fibrillation; diagnosis automation; discrete time measurements; fuzzy memberships; general methodology; hierarchical fuzzy automatons; input tokens; lower level automatons; signal representation; Artificial neural networks; Atrial fibrillation; Automata; Electrocardiography; Fuzzy sets; Performance evaluation; Resonance; Signal processing; Subspace constraints; Time measurement;
Conference_Titel :
Fuzzy Information Processing Society - NAFIPS, 1998 Conference of the North American
Conference_Location :
Pensacola Beach, FL
Print_ISBN :
0-7803-4453-7
DOI :
10.1109/NAFIPS.1998.715597