A novel controllable energy constraints-variational mode decomposition denoising algorithm

Electrocardiogram (ECG) is mainly utilized for diagnosing heart diseases. However, various noises can influence the diagnostic accuracy. This paper presents a novel algorithm for denoising ECG signals by employing the Controlled Energy Constraint-Variational Mode Decomposition (CEC-VMD). Firstly, the noisy ECG signal is decomposed using CEC-VMD to obtain a set of intrinsic mode functions (IMFs) and a residual r. A modulation factor is utilized to minimize the modal information contained in the decomposed residuals. Furthermore, this paper presents an update formula for the modal and central frequencies based on ADMM. Finally, all the IMFs are integrated to obtain the ECG signal after denoising. By varying the value of the modulation factor, not only is the spectral energy loss of each mode reduced, but the orthogonality between the modes is also improved to better concentrate the energy of each mode. The experiments on simulated signals and MIT-BIH signals show that the average SNR after CEC-VMD denoising is 22.5139, the RMSE is 0.1128, and the CC is 0.9882. In addition, the proposed algorithm effectively improves the classification accuracy values, which are 99.0% and 99.9% for the SVM and KNN classifiers, respectively. These values are improved compared with those of EMD, VMD, SWT, SVD-VMD, and VMD-SWT. The proposed CEC-VMD technique for denoising ECG signals removes noise and better preserves features.