Application of Bayesian Ying-Yang criteria for selecting the number of hidden units with backpropagation learning to electrocardiogram classification

Computer electrocardiography (EGG) is a fundamental diagnostic method for both contour and rhythm analysis. For patients with Hypertrophic Cardiomyopathy (HCM), there are more or less abnormal ECG findings. However, to diagnose HCM through ECG is a difficult task even for experienced cardiologist. Backpropagation has been used for ECG classification with the number of hidden units chosen heuristically. In this paper, the hidden unit number is selected by a new criteria obtained from the so-call Bayesian Ying-Yang learning theory and applied in ECG classification to diagnose HCM. Experiments have shown that the selected number is highly consistent with the minimal generalization error and the corresponding architecture show best classification performance