School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, China.
School of Computer, Electronics and Information, Guangxi University, Nanning 530004, China.
Biomed Res Int. 2021 Mar 15;2021:6624298. doi: 10.1155/2021/6624298. eCollection 2021.
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 magnitude, while the RMSE and MAE of some competitive prediction methods are of 10 magnitude. Compared with other several prediction methods, our method obviously improves the prediction accuracy of ECG signals.
为了探索在体域网（BANs）中预测心电图信号的方法，我们在本文中提出了一种用于心电图信号的混合预测方法。该方法结合变分模态分解（VMD）、相空间重构（PSR）和径向基函数（RBF）神经网络来预测心电图信号。为了降低心电图信号的非平稳性和随机性，我们使用 VMD 将心电图信号分解为几个具有有限带宽的固有模态函数（IMF），这有助于提高预测精度。RBF 神经网络的输入参数会影响预测精度和计算负担。我们采用 PSR 来优化 RBF 神经网络的输入参数。为了评估所提出方法的预测性能，我们在麻省理工学院-贝斯以色列医院心律失常数据库中的心电图数据上进行了多次仿真实验。实验结果表明，所提出方法的均方根误差（RMSE）和平均绝对误差（MAE）为 10 数量级，而一些竞争预测方法的 RMSE 和 MAE 为 10 数量级。与其他几种预测方法相比，我们的方法明显提高了心电图信号的预测精度。
