Electrocardiogram prediction based on variational mode decomposition and a convolutional gated recurrent unit

Abstract

Electrocardiogram (ECG) prediction is highly important for detecting and storing heart signals and identifying potential health hazards. To improve the duration and accuracy of ECG prediction on the basis of noise filtering, a new algorithm based on variational mode decomposition (VMD) and a convolutional gated recurrent unit (ConvGRU) was proposed, named VMD-ConvGRU. VMD can directly remove noise, such as baseline drift noise, without manual intervention, greatly improving the model usability, and its combination with ConvGRU improves the prediction time and accuracy. The proposed algorithm was compared with three related algorithms (PSR-NN, VMD-NN and TS fuzzy) on MIT-BIH, an internationally recognized arrhythmia database. The experiments showed that the VMD-ConvGRU algorithm not only achieves better prediction accuracy than that of the other three algorithms but also has a considerable advantage in terms of prediction time. In addition, prediction experiments on both the MIT-BIH and European ST-T databases have shown that the VMD-ConvGRU algorithm has better generalizability than the other methods.