Title :
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
fDate :
May 30 2007-June 1 2007
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;
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
DOI :
10.1109/ICCA.2007.4376536
