Neural Variational Mode Decomposition and Its Application for ECG Denoising

Variational mode decomposition (VMD) is a widely used method for analyzing and denoising temporal and non-stationary signals. Several extensions of VMD, such as the wavelet transform with VMD (VMD-DWT), non-local means with VMD (VMD-NLM), and their combination (VMD-DWT-NLM), have demonstrated satisfactory performance. However, these VMD-based methods often require substantial online computation due to the non-linear decomposition process, especially when processing large datasets. To address this challenge, this study proposes a novel approach called neural VMD (NVMD), which integrates VMD’s decomposition capabilities with the powerful feature extraction of neural networks (NN), while adaptively selecting the optimal number of intrinsic mode functions (IMFs) for temporal signal analysis and denoising. Two systems, NVMD(A) and NVMD(P), were developed, incorporating autoencoder-based NN and progressive NN, respectively. We evaluated the proposed NVMD framework on the task of ECG signal denoising using the MIT-BIH dataset, contaminated with various noise types and different signal-to-noise ratio (SNR) levels. Experimental results show that the proposed method significantly improves the SNR, reduces computational complexity, and adaptively selects the optimal number of IMFs for effective ECG denoising.