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.