<abstract xml:lang="en" xmlns="http://www.w3.org/1999/xhtml"><p>Coronary artery disease is due to atheromatous narrowing and subsequent occlusion of the coronary vessel. 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 was. 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 especially optimised and a thoroughly verified 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.</p></abstract>

Coronary artery disease is due to atheromatous narrowing and subsequent occlusion of the coronary vessel. 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 was. 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 especially optimised and a thoroughly verified 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.

Coronary Artery Diagnosis Aided by Neural Network

Institute of Precision and Biomedical Engineering, Faculty of Mechatronics, Warsaw University of Technology, Św. A. Boboli 8, 02-525 Warsaw, Poland

Polish Journal of Medical Physics and Engineering

{"article-title":"Coronary Artery Diagnosis Aided by Neural Network"}

Coronary artery disease is due to atheromatous narrowing and subsequent occlusion of the coronary vessel. Application of optimised feed forward multi-layer...

Coronary Artery Diagnosis Aided by Neural Network

Pubblicato online: 30 dic 2008

Pagine: 149 - 155

DOI: https://doi.org/10.2478/v10013-007-0013-6

Parole chiave
coronary artery disease, ECG exercise test, MLBP, computer aided diagnosis, artificial neural networks

This content is open access.

Coronary Artery Diagnosis Aided by Neural Network

Pubblicato online: 30 dic 2008

Pagine: 149 - 155

DOI: https://doi.org/10.2478/v10013-007-0013-6

Parole chiavecoronary artery disease, ECG exercise test, MLBP, computer aided diagnosis, artificial neural networks

This content is open access.

Parole chiave
coronary artery disease, ECG exercise test, MLBP, computer aided diagnosis, artificial neural networks