A hybrid neuro-fuzzy system for ECG classification of myocardial infarction

Author

Bozzola, P. ; Bortolan, G. ; Combi, C. ; Pinciroli, F. ; Brohet, C.

Author_Institution

Milano, Italy

fYear

1996

Firstpage

241

Lastpage

244

Abstract

We present an approach to the automated ECG classification based on a hybrid neuro-fuzzy model. The classification power of the connectionist paradigm has been coupled with the ability of the fuzzy set formalism to treat in a quantitative way natural language. This allows us to build up a system capable of both a good classification accuracy and to give meaningful explanations of the proposed diagnoses, in the form of symbolic IF-THEN rules.

Keywords

adaptive signal processing; electrocardiography; feedforward neural nets; fuzzy set theory; medical signal processing; multilayer perceptrons; muscle; pattern classification; ECG classification; automated ECG classification; classification accuracy; classification power; connectionist paradigm; fuzzy set formalism; hybrid neuro-fuzzy system; multilayer perceptron model; myocardial infarction; natural language; symbolic IF-THEN rules; Adaptive systems; Cardiology; Databases; Electrocardiography; Fuzzy neural networks; Libraries; Multilayer perceptrons; Myocardium; Natural languages; Neural networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Computers in Cardiology, 1996

Conference_Location

Indianapolis, IN, USA

ISSN

0276-6547

Print_ISBN

0-7803-3710-7

Type

conf

DOI

10.1109/CIC.1996.542518

Filename

542518

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1674623