Data Prediction of ECG Based on Phase Space Reconstruction and Neural Network

Sun, Zhuo; Wang, QiaoLing; Xue, QuanDe; Li, Qun; Tan, Qingquan

doi:10.1109/iceiec.2018.8473502

Cited by 12 publications

(23 citation statements)

References 10 publications

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“…In the method, the PSR theory and the TS fuzzy model were used to predict an ECG signal. The accuracy of the prediction is close to that of [17] and [18].…”

mentioning

confidence: 53%

“…As shown in Table 3, the RMSE and MAE of the proposed method are 10 -3 magnitude, while the RMSE and MAE of [17], [18], and [24] are 10 -2 magnitude. Figure 8 shows that the RMSE and MAE of the proposed method are significantly smaller than those of [17]， [18] , and [24].This illustrates that the prediction accuracy of this paper is much higher than that of [17], [18], and [24]. We also compared the proposed method with some traditional prediction models, such as ARMA, LS-SVM, SVM, and Kalman.…”

Section: Figure 8 Comparing With Other Prediction Methodsmentioning

confidence: 98%

“…To illustrate the advantages of the proposed prediction method, we compared it with the methods of Sun et al [17], Sun et al [18] and Su et al [24]. We experimented on the same data source, No.…”

Section: Comparison With Some Traditional Prediction Methodsmentioning

confidence: 99%

“…100 ECG, and the experimental results were shown in Table 3 and Figure 8. [17] 0.0233 0.0157 Sun et al [18] 0.0423 0.0240 Su et al [24] 0.0146 0.0106…”

Section: Comparison With Some Traditional Prediction Methodsmentioning

confidence: 99%

“…In [17], Sun et al proposed a prediction method of an ECG signal using an error backpropagation neural network (BPNN) and VMD technology. Sun et al [18] proposed another prediction method of an ECG signal. The method was based on PSR theory and BPNN, with accuracy close to that of the previously mentioned method.…”

mentioning

confidence: 99%

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Hybrid prediction method of ECG signals based on VMD, PSR, and RBF neural network

Fu-ying

Qin

Wang

et al. 2020

Preprint

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Background: It is significant for doctors and body area networks (BANs) to predict ECG signals accurately. At present, the prediction accuracy of many existing ECG prediction methods is generally low. In order to improve the prediction accuracy of ECG signals in BANs, a hybrid prediction method of ECG signals is proposed in this paper. Methods: The proposed prediction method combines variational mode decomposition (VMD), phase space reconstruction (PSR), and a radial basis function (RBF) neural network. First, the embedding dimension and delay time of PSR are calculated according to the trained set of ECG data. Second, the ECG data are decomposed into several intrinsic mode functions (IMFs). Third, the phase space of each IMF is reconstructed according to the embedding dimension and the delay time. Fourth, an RBF neural network is established and each IMF is predicted by the network. Finally, the prediction results of all IMFs are added to realize the final prediction result. Results: To evaluate the prediction performance of the proposed method, simulation experiments are carried out on ECG data from the MIT-BIH Arrhythmia Database. The experimental results show that the prediction index RMSE (root mean square error) of the proposed method is only 10-3 magnitude and that of some traditional prediction methods is 10-2 magnitude.Conclusions: Compared with some traditional prediction methods, the proposed method improves the prediction accuracy of ECG signals obviously.

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“…In the method, the PSR theory and the TS fuzzy model were used to predict an ECG signal. The accuracy of the prediction is close to that of [17] and [18].…”

mentioning

confidence: 53%

Section: Figure 8 Comparing With Other Prediction Methodsmentioning

confidence: 98%

Section: Comparison With Some Traditional Prediction Methodsmentioning

confidence: 99%

“…100 ECG, and the experimental results were shown in Table 3 and Figure 8. [17] 0.0233 0.0157 Sun et al [18] 0.0423 0.0240 Su et al [24] 0.0146 0.0106…”

Section: Comparison With Some Traditional Prediction Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

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Hybrid prediction method of ECG signals based on VMD, PSR, and RBF neural network

Fu-ying

Qin

Wang

et al. 2020

Preprint

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Early arrhythmia prediction based on Hurst index and ECG prediction using robust LMS adaptive filter

Ashkezari-Toussi

Sabzevari

2021

SIViP

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Recognition of partial discharge of cable accessories based on convolutional neural network with small data set

Zhang

He²,

Lin

et al. 2020

COMPEL

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Purpose Considering the problem that the high recognition rate of deep learning requires the support of mass data, this study aims to propose an insulating fault identification method based on small data set convolutional neural network (CNN). Design/methodology/approach Because of the chaotic characteristics of partial discharge (PD) signals, the equivalent transformation of the PD signal of unit power frequency period is carried out by phase space reconstruction to derive the chaotic features. At the same time, geometric, fractal, entropy and time domain features are extracted to increase the volume of feature data. Finally, the combined features are constructed and imported into CNN to complete PD recognition. Findings The results of the case study show that the proposed method can realize the PD recognition of small data set and make up for the shortcomings of the methods based on CNN. Also, the 1-CNN built in this paper has better recognition performance for four typical insulation faults of cable accessories. The recognition performance is improved by 4.37% and 1.25%, respectively, compared with similar methods based on support vector machine and BPNN. Originality/value In this paper, a method of insulation fault recognition based on CNN with small data set is proposed, which can solve the difficulty to realize insulation fault recognition of cable accessories and deep data mining because of insufficient measure data.

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Data Prediction of ECG Based on Phase Space Reconstruction and Neural Network

Cited by 12 publications

References 10 publications

Hybrid prediction method of ECG signals based on VMD, PSR, and RBF neural network

Hybrid prediction method of ECG signals based on VMD, PSR, and RBF neural network

Early arrhythmia prediction based on Hurst index and ECG prediction using robust LMS adaptive filter

Recognition of partial discharge of cable accessories based on convolutional neural network with small data set

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