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Classification is the basis of electrocardiography (ECG) analysis. In the last decades, a large number of methods were proposed to deal with the classification of ECG beats. In this paper a kind of deep learning method is introduced into ECG beats classification. We create a classifier with stacked sparse autoencoder (SAE), and then combine the softmax regression with the SAE networks to consummate...
Aiming at the shorting of the existing atrial fibrillation (AF) detection algorithms and improve the ability of intelligent recognition and extraction of AF signals. Recently, deep learning theory with massive data has been used on image, voice and other filed widely. In this paper, a method based on the stack sparse autoencoder neural network, a instance of deep learning strategy, was proposed for...
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