A Deep Learning Method for ECG Signal Prediction Based on VMD, Cao Method, and LSTM Neural Network

Abstract

Background: In body area network (BAN), accurate prediction of ECG signal can not only let doctors know the patient's condition in advance, but also help to reduce the energy consumption of sensors. In order to improve the accuracy of ECG signal prediction, this paper proposes a deep learning method for ECG signal prediction. Methods: The proposed prediction method combines variational mode decomposition (VMD), Cao method and a long short-term memory (LSTM) neural network. In the method, VMD decomposes ECG data into a series of intrinsic mode functions (IMFs), which reduces the non-stationary character of ECG signals and helps to improve the prediction accuracy. Cao method is used to determine the input dimension of LSTM input layer, namely, the minimum embedding dimension of each IMF is the input dimension of LSTM input layer. Each IMF is predicted by a LSTM neural network which adopts Adam optimizer. All IMFs predictions are aggregated to get the final prediction result. Results: To evaluate the prediction accuracy of the proposed method, simulation experiments are carried out on ECG data from the MIT-BIH Arrhythmia Database. Experimental results show that the RMSE (root mean square error) and MAE (mean absolute error) of the proposed model are 0.001326 and 0.001044 respectively, which are more than 10 percent lower than the traditional prediction methods.Conclusions: Compared with some traditional prediction methods, the proposed prediction method improves the prediction accuracy obviously.

Electrocardiogram Prediction Based on Variational Modal Decomposition and Convolutional Recurrent Gate Unit

“…The method proposed by Huang F et al in 2021 [10] improved the accuracy of ECG prediction with RMSE and MAE of 0.001326 and 0.001044 for 100 data, respectively.…”

“…The difference is that the size of the one-dimensional convolution kernel is parameters = in channels × out channels × kernal size (9) The classical GRU based on matrix multiplication can be denoted as Eq. (10).…”

“…In 2018, the team also proposed a prediction based on phase space reconstruction and neural network [9], and still used same example(No.100) in MIT-BIH database for experiments to show the effectiveness of the algorithm. Huang et al proposed an ECG signal prediction method combining VMD, Cao and LSTM neural network, which significantly improved the prediction accuracy [10].…”

Monthly Runoff Prediction for Xijiang River via Gated Recurrent Unit, Discrete Wavelet Transform, and Variational Modal Decomposition

“…This trend suggests that starting from K = 6, the occurrence of modal overlap may become apparent. Conversely, if K is too small, insufficient decomposition may result [38]. Consequently, K = 5 is determined as the optimal count of decomposition layers for VMD.…”

An Long Short-Term Memory Model with Multi-Scale Context Fusion and Attention for Radar Echo Extrapolation

“…In recent times, deep learning has become the most rapidly developing technique in machine learning, and in response to the problems of traditional methods, more and more people are trying to use deep learning methods to solve video prediction problems [9][10][11], traffic flow prediction [12][13][14] and precipitation nowcasting [15][16][17][18][19][20][21][22], as well as other spatiotemporal sequence prediction problems. Deep learning methods can handle complex spatio-temporal relationships in order to adaptively learn the patterns of rainfall variability from a large number of previous radar echo sequences.…”