Tytuł artykułu
Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
Improving the classification accuracy of electrocardiogram (ECG) signals is of great significance for diagnosing heart abnormalities and arrhythmias and preventing cardiovascular diseases (CVDs). The traditional classification method depends on medical experience to select and extract features artificially, lacks the generalization ability to deal with big medical data. The vital feature extraction ability of neural networks has become a hot topic to solve this problem. Based on this, the capsule network is applied to ECG signal classification in this paper. Based on the original network architecture, long short-term memory (LSTM) network and 1D convolutional neural network (CNN) are added as a parallel feature extraction layer to extract the spatial and temporal features of the ECG signal. In addition, the enhanced routing algorithm is proposed, which uses the prior probability of subcapsules as a weighting factor for routing algorithm classification to weaken the influence of noise capsules. The proposed model is superior to the existing state-of-the-art techniques when tested on the MIT-BIH arrhythmia database.
Wydawca
Czasopismo
Rocznik
Tom
Strony
543--555
Opis fizyczny
Bibliogr. 44 poz., rys., tab., wykr.
Twórcy
autor
- School of Computer Science and Engineering, Central South University, Changsha, China
autor
- School of Computer Science and Engineering, Central South University, Changsha, China
autor
- School of Computer Science and Engineering, Central South University, Changsha 410083, China
Bibliografia
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- [43] The source codes and other related algorithms. https://github.com/jiayoying/ECG_CAP.
- [44] K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition, 2015. http://arxiv. org/abs/1409.1556.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-f7025aa8-667f-4b7e-8f2a-0eae36fd094b