Hybrid Prediction Method for ECG Signals Based on VMD, PSR, and RBF Neural Network.
To explore a method to predict ECG signals in body area networks (BANs), we propose a hybrid prediction method for ECG signals in this paper. The proposed method combines variational mode decomposition (VMD), phase space reconstruction (PSR), and a radial basis function (RBF) neural network to predi...
| Publicado en: | BioMed Research International pp. 1 - 14 |
|---|---|
| Autores principales: | , , , |
| Formato: | equations & formulas research tables/charts tracings Journal Article |
| Publicado: |
Wiley-Blackwell
3/16/2021
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=149314933&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 149314933 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 3/16/2021 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 149314933 149314933 149314933 10.1155/2021/6624298 149314933 ppf: 1 ppct: 13 formats: fmt: @attributes: type: P tig: atl: Hybrid Prediction Method for ECG Signals Based on VMD, PSR, and RBF Neural Network. aug: au: Huang, Fuying Qin, Tuanfa Wang, Limei Wan, Haibin affil: School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, China sug: subj: Electrocardiography Neural Networks (Computer) Wireless Communications Signal Processing, Computer Assisted Prediction Models Methods Human Predictive Validity Models, Statistical Arrhythmia ab: To explore a method to predict ECG signals in body area networks (BANs), we propose a hybrid prediction method for ECG signals in this paper. The proposed method combines variational mode decomposition (VMD), phase space reconstruction (PSR), and a radial basis function (RBF) neural network to predict an ECG signal. To reduce the nonstationarity and randomness of the ECG signal, we use VMD to decompose the ECG signal into several intrinsic mode functions (IMFs) with finite bandwidth, which is helpful to improve the prediction accuracy. The input parameters of the RBF neural network affect the prediction accuracy and computational burden. We employ PSR to optimize input parameters of the RBF neural network. To evaluate the prediction performance of the proposed method, we carry out many simulation experiments on ECG data from the MIT-BIH Arrhythmia Database. The experimental results show that the root mean square error (RMSE) and mean absolute error (MAE) of the proposed method are of 10-3 magnitude, while the RMSE and MAE of some competitive prediction methods are of 10-2 magnitude. Compared with other several prediction methods, our method obviously improves the prediction accuracy of ECG signals. pubtype: Academic Journal doctype: equations & formulas research tables/charts tracings Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|