Possibilities of using neural networks for ECG classification.
Some characteristics of the neural network approach have been tested and validated for the particular problem of diagnostic classification in the field of computerized electrocardiography. Two different databases have been used for the evaluation process: CORDA, developed by the Medical Informatics Department of the University of Leuven, and ECG-UCL, developed by the Cliniques Universitaires Saint-Luc, Université Catholique de Louvain. Electrocardiographic signals classified on the basis of electrocardiographic independent clinical data, with a single diagnosis and no conduction abnormalities, have been considered. Seven diagnostic classes have been taken into account, including the different locations of ventricular hypertrophy and myocardial infarction. Two architectures of neural networks have been analyzed in detail considering three aspects: the normalization process, pruning techniques, and fuzzy preprocessing by the use of radial basis functions. The comparison of the results obtained with the two databases will be discussed in detail.
Bortolan
G
G
LADSEB-CNR, Padova, Italy.
Brohet
C
C
Fusaro
S
S
D000465:Algorithms; D006332:Cardiomegaly / Q000175:diagnosis / Q000503:physiopathology; D004562:Electrocardiography / Q000145:classification* / Q000379:methods; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D009203:Myocardial Infarction / Q000175:diagnosis / Q000503:physiopathology; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted