DocumentCode :
2177420
Title :
A diagnosis methodology for continuous time measurements using hierarchical signal representations
Author :
Tümer, M. Borahan ; Belfore, Lee A., II ; Ropella, Kristina M.
Author_Institution :
Dept. of Electr. & Comput. Eng., Marquette Univ., Milwaukee, WI, USA
Volume :
3
fYear :
1998
fDate :
11-14 Oct 1998
Firstpage :
3038
Abstract :
A methodology for automated diagnosis of systems characterized by continuous signals is presented. The methodology requires the definition and construction of several fuzzy automatons each capable of identifying a particular condition. When the diagnostic system is in operation, the time sampled system measurements are presented to all automatons simultaneously. The fuzziness in automaton operation enables input processing from several perspectives, consistent with the operation of the automatons, allowing for toleration of measurement noise and other ambiguities. The methodology is applied to the problem of automatic electrocardiogram diagnosis
Keywords :
ART neural nets; automata theory; electrocardiography; fuzzy set theory; medical diagnostic computing; ART neural net; ECG; automated diagnosis; continuous time measurements; electrocardiogram diagnosis; fuzzy automaton; fuzzy set theory; hierarchical signal representations; medical diagnosis; Automata; Biomedical computing; Biomedical engineering; Biomedical measurements; Medical diagnostic imaging; Noise measurement; Nonlinear systems; Signal processing; Signal representations; Time measurement;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Systems, Man, and Cybernetics, 1998. 1998 IEEE International Conference on
Conference_Location :
San Diego, CA
ISSN :
1062-922X
Print_ISBN :
0-7803-4778-1
Type :
conf
DOI :
10.1109/ICSMC.1998.725127
Filename :
725127
Link To Document :
بازگشت