Vector-Based Postprocessing Method for Improving ECG Denoising Techniques by Re-Establishing Lead Relationships

Reducing noise in electrocardiogram (ECG) signals is essential for accurate diagnosis and monitoring of cardiac diseases. Existing denoising methods often denoise leads individually and distort Einthoven’s law due to their limitations in distinguishing between noise and signal components across all leads. In this article, a new method has been proposed that not only restores the Einthoven relationship but also effectively reduces the remaining noises of the denoised signals. We introduced the weighted average cardiac vector, calculated from denoised signals, and used it to reconstruct lead signals. Our method is applicable to all denoising processes to improve the denoised limb lead values and reduce the error signals. In experiments involving empirical mode decomposition (EMD) and wavelet transform, our postprocessing achieved an average noise reduction of 28.6% and 10.8%, respectively. To assess our method in practice, we build two CNN-based diagnosis detection models (standard and ResNet-18). When our method was used alongside the wavelet transform, the standard CNN’s accuracy improved from 63% to 70%, while the ResNet’s accuracy increased from 73.31% to 77.41%. In the case of EMD, our method enhanced the standard CNN’s accuracy from 79.8% to 84.5% and the ResNet’s accuracy from 89.78% to 93.84%. These promising results recommend our algorithm as a valuable postprocessing unit for denoising systems.