Comparative Study of ECG Denoising Methods for Wearable Applications
arXiv:2607.11450
The paper compares model-based (EMD variants and DWT) and deep learning (SDAE and PINN) methods for denoising upper-arm ECG signals affected by EMG interference in wearable and space settings.
Abstract
Reliable electrocardiogram (ECG) monitoring in wearable and space environments requires effective denoising of signals corrupted by non-stationary electromyogram (EMG) interference. This paper presents a comparative evaluation of model-based and DL-based denoising techniques for upper-arm ECG recordings acquired under real conditions. The model-based methods include three empirical mode decomposition (EMD) variants and a discrete wavelet transform (DWT) approach, while the deep learning (DL) side is represented by a stacked denoising autoencoder (SDAE) and a physics-informed neural network (PINN). All methods are evaluated on real acquisitions under both relaxed and voluntary muscle contraction conditions, using root mean squared error (RMSE), Pearson correlation, and peak-to-peak signal-to-noise ratio (PPSNR) as performance metrics. Results reveal a fundamental trade-off: DL methods achieve superior morphological reconstruction, while DWT provides the strongest noise suppression, highlighting complementary strengths for wearable cardiac monitoring applications.
Accepted for presentation at 14th Annual IEEE International Conference on Wireless for Space and Extreme Environments (WISEE 2026)