Application of MLBP Neural Network for Exercise ECG Test Records Analysis in Coronary Artery Diagnosis

Abstract

Atheromatous narrowing and subsequent occlusion of the coronary vessel cause coronary artery disease. Application of optimised feed forward multi-layer back propagation neural network (MLBP) for detection of narrowing in coronary artery vessels is presented in this paper. The research was performed using 580 data records from traditional ECG exercise test confirmed by coronary arteriography results. Each record of training database included description of the state of a patient providing input data for the neural network. Level and slope of ST segment of a 12 lead ECG signal recorded at rest and after effort (48 floating point values) was the main component of input data for neural network. Coronary arteriography results (verified the existence or absence of more than 50% stenosis of the particular coronary vessels) were used as a correct neural network training output pattern. More than 96% of cases were correctly recognised by thoroughly verified MLBP neural network. Leave one out method was used for neural network verification so 580 data records could be used for training as well as for verification of neural network.