Computerised electrocardiology employing bi-group neural networks.
A configuration of bi-group neural networks (BGNN) is proposed combined with an evidential reasoning framework to interpret 12-lead electrocardiograms for three mutually exclusive classes. A number of pre-processing feature selection techniques were investigated prior to application of the input feature vector to each individual BGNN. The network outputs were discounted within a belief interval of 1 based on their performance on test data prior to combination. It was found that the application of the feature selection techniques enhanced the individual performance of the BGNN, and subsequently enhanced the overall performance. The proposed framework was compared with conventional classification techniques of multi-output neural networks and linear multiple regression. The framework attained a higher level of classification in comparison with the other methods; 70.4% compared with 66.7% for both multi-output neural and statistical techniques.
Nugent
C D
CD
The Northern Ireland Bio-Engineering Centre, School of Electrical and Mechanical Engineering, University of Ulster at Jordanstown, Newtownabbey, UK. cd.nugent@ulst.ac.uk
Webb
J A
JA
McIntyre
M
M
Black
N D
ND
Wright
G T
GT
D003937:Diagnosis, Differential; D004562:Electrocardiography / Q000145:classification*; D006331:Heart Diseases / Q000175:diagnosis; D006801:Humans; D015999:Multivariate Analysis; D016571:Neural Networks, Computer