A Deep Learning Prediction Method for ECG Signals Using VMD, Cao Method, and LSTM Neural Network

Accurate prediction of Electrocardiogram (ECG) signals is crucial for early diagnosis and continuous cardiac monitoring. To address the challenges posed by the non-stationary nature of ECG signals, this study introduces a novel deep learning prediction method that synergistically combines Variational Mode Decomposition (VMD), the Cao method, and a Long Short-Term Memory (LSTM) neural network. The proposed method first applies VMD to adaptively decompose the raw ECG signal into several Intrinsic Mode Functions (IMFs), which effectively reduces nonlinearity and non-stationarity. Subsequently, the Cao method is utilized to compute the minimum embedding dimension for each IMF, thereby optimally configuring the input structure for the LSTM network. Each IMF component is then predicted independently by an LSTM model trained with the Adam optimizer. The final reconstructed ECG signal is derived from the aggregate of these individual IMF predictions. Evaluated on the benchmark MIT-BIH Arrhythmia Database, the proposed method achieves a root mean square error (RMSE) of 0.001326 and a mean absolute error (MAE) of 0.001044, demonstrating high predictive precision. The results from a comparative study indicate that the proposed method surpasses several established prediction methods, confirming its effectiveness and potential for practical application in enhancing ECG signal analysis.