Research on Diagnosing Coronary Heart Disease using Fuzzy Adaptive Resonance Theory Mapping Neural Networks

Author

Shi, Li ; Sun, Zhifu ; Li, Hui ; Liu, Wei

Author_Institution

Zhengzhou Univ., Zhengzhou

fYear

2007

fDate

May 30 2007-June 1 2007

Firstpage

1126

Lastpage

1128

Abstract

ST segment is the most important diagnostic parameter for finding coronary heart disease (CHD). Based on ST segment which has been extracted from electrocardiogram (ECG) data with wavelet transform, we investigated the classification of five different shapes of ST segment using fuzzy adaptive resonance theory mapping (ARTMAP) neural networks. The proposed method was demonstrated by the data from the standard MIT/BIH ECG database. The results show that fuzzy ARTMAP could be used to distinguish the shapes of ST segment successfully.

Keywords

ART neural nets; diseases; electrocardiography; fuzzy neural nets; medical diagnostic computing; ST segment; coronary heart disease; electrocardiogram; fuzzy ARTMAP; fuzzy adaptive resonance theory mapping; neural network; wavelet transform; Cardiac disease; Data mining; Databases; Electrocardiography; Fuzzy neural networks; Neural networks; Resonance; Shape; Subspace constraints; Wavelet transforms; BP network; ECG; Fuzzy ARTMAP; ST segment; coronary heart disease;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Automation, 2007. ICCA 2007. IEEE International Conference on

Conference_Location

Guangzhou

Print_ISBN

978-1-4244-0818-4

Electronic_ISBN

978-1-4244-0818-4

Type

conf

DOI

10.1109/ICCA.2007.4376536

Filename

4376536