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In this paper, we propose a scheme to integrate independent component analysis (ICA) and neural networks for electrocardiogram (ECG) beat classification. The ICA is used to decompose ECG signals into weighted sum of basic components that are statistically mutual independent. The projections on these components, together with the RR interval, then constitute(More)
In this paper, we propose a novel independent components (ICs) arrangement strategy to cooperate with the independent component analysis (ICA) method used for ECG beat classification. The ICs calculated with a regular ICA algorithm are rearranged according to the L 2 norms of the rows of the de-mixing matrix. The validity of this ICs arrangement strategy is(More)
We propose a method that uses independent component analysis (ICA) and backpropagation neural network to classify electrocardiogram (ECG) signals. In this study, ICA is used to extract important features from ECG signals. A backpropagation neural network follows to classify the input ECG beats into one of eight beat types. The independent components are(More)
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