Deep learning approach for active classification of electrocardiogram signals

In this paper, we propose a novel approach based on deep learning for active classification of electrocardiogram (ECG) signals. To this end, we learn a suitable feature representation from the raw ECG data in an unsupervised way using stacked denoising autoencoders (SDAEs) with sparsity constraint....

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Vydané v:	Information sciences Ročník 345; s. 340 - 354
Hlavní autori:	Rahhal, M.M. Al, Bazi, Yakoub, AlHichri, Haikel, Alajlan, Naif, Melgani, Farid, Yager, R.R.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Inc 01.06.2016
Predmet:	Active learning (AL) Classification Deep neural network (DNN) Denoising autoencoder (DAE) ECG signal classification Entropy Feature learning Learning Neural networks Recommendations Representations Retraining ECG signal classification Feature learning Denoising autoencoder (DAE) Active learning (AL) Deep neural network (DNN)
ISSN:	0020-0255, 1872-6291
On-line prístup:	Získať plný text
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