Variational mode decomposition with nonlocal means technique for robust denoising ECG signal

Removing noise from ECG signals is still a very frequently discussed topic. There are many challenges that make this problem more complex. These challenges are driven by the widespread use of ECG devices such as monitoring a heart disease of patients and a heart rate of athletes namely when reading and sending ECG signal data is done, there is a lot of noise that contaminates ECG signals, one of which is white Gaussian noise. In this research, noise elimination is done by using Variation mode decomposition (VMD) and Nonlocal Means (NLM) method. Denoising using the NLM technique approach is very effective in removing white Gaussian noise but it suffers from the issue of under-Averaging in the high-frequency QRS-complex region and the VMD method which is the development of the Empirical Mode Decomposition (EMD) method has better capability to decompose the signal into several modes. By using the advantages of each method, we are capable of achieving good performance in removing noise from the ECG signals. Testing of the proposed method was performed using MIT-BIH arrhythmias database, which was given Gaussian white noise with different SNR levels. Measurements were made using three levels of measurement matrices i.e. Signal for Ratio Noise Enhancement (SNRimp), Mean Square Error (MSE) and Percentage root mean square difference (PRD). In addition, we may use methods that are compatible with state-of-The-Art noise removal methods on ECG signals i.e. The DWT thresholding, EMD soft thresholding, EMD + Wavelet, NLM, M-EMD+NLM, and DWT+NLM. The end result is a method that is capable of producing a slightly higher SNRimp, a lower MSE and PRD value.