An Intelligent System for Noninvasive Diagnosis of Coronary Artery Disease with EMD-TEO and BP Neural Network

Authors:
Zhidong Zhao;Chan Ma
Affiliations:
-;-
Venue:
ETTANDGRS '08 Proceedings of the 2008 International Workshop on Education Technology and Training & 2008 International Workshop on Geoscience and Remote Sensing - Volume 02
Year:
2008

Citing 0
Cited 3

Automated diagnosis of Coronary Artery Disease affected patients using LDA, PCA, ICA and Discrete Wavelet Transform

Knowledge-Based Systems
Automated classification of patients with coronary artery disease using grayscale features from left ventricle echocardiographic images

Computer Methods and Programs in Biomedicine
Linear and nonlinear analysis of normal and CAD-affected heart rate signals

Computer Methods and Programs in Biomedicine

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Abstract

A novel intelligent noninvasive diagnosis system of Coronary Artery Disease (CAD) is proposed based on Empirical Mode Decomposition (EMD)－Teager Energy Operator (TEO) and Back-Propagation (BP) neural network. The occluded arteries can produce the diastolic murmurs with high frequency energy. Firstly, the instantaneous frequency of the diastolic murmurs is estimated by EMD－TEO to identify features associated with coronary stenoses. Secondly, statistical quantities of Instantaneous frequency are extracted which are used as feature vectors relating to normal and abnormal recordings. In end, the BP neural network classifier is build to classify the extracted features. The performance of the developed system has been evaluated in clinic recordings. The correct classification rate is over 85% for normal and abnormal subjects. The results show that the intelligent noninvasive system based on EMD-TEO and BP neural network can be used as an effective tool to diagnose Coronary Artery.