Capsule network assisted electrocardiogram classification model for smart healthcare

• Autorzy: Jiao Ying , Qi Huamei , Wu Jia

Identyfikatory

DOI

10.1016/j.bbe.2022.03.006

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

capsule network; ECG; electrocardiogram; LSTM; long short term memory; CNN; convolutional neural network

PL

sieć kapsułowa; EKG; elektrokardiogram; LSTM; CNN; sieć neuronowa konwolucyjna

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2022
• Tom: Vol. 42, no. 2
• Strony: 543--555
• Opis fizyczny: Bibliogr. 44 poz., rys., tab., wykr.

Twórcy

autor

Jiao Ying

• School of Computer Science and Engineering, Central South University, Changsha, China

autor

Qi Huamei

• School of Computer Science and Engineering, Central South University, Changsha, China

autor

Wu Jia

• School of Computer Science and Engineering, Central South University, Changsha 410083, China

Bibliografia

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