Year: 2025
Type: conference-paper
Abstract: Motion Artifact (MA) noise is one of the most crucial components in an Electrocardiogram (ECG) signal, especially during the monitoring of normal daily activities. Because of this, they are widely investigated for optimized denoising applications, trying to maximize the physiological information while solving the noise-signal frequency overlapping. In this work, we propose a filtering approach that employs the Multilevel Discrete Wavelet Decomposition (MDWD) basis domain, in which the projections of the signal are easily separable from the noise components. Compared to other more complex denoising approaches, this method only requires the simple projection of the signal on the desired wavelet basis. We obtain the desired denoising effect through the elimination of part of the projected signal, i.e., we remove the projected coefficients with the largest scaling values. We show that these coefficients carry most of the noise introduced by MA. To validate the method and tune its parameters, we test ECG affected by MA from different datasets, proving that the reconstruction performance is on par with the state-of-the-art approaches, such as the Empirical Wavelet Transform method (EWT), while begin much simpler in practice. Moreover, while other approaches tend to destroy signal anomalies and non-idealities which are fundamental for diagnosis, our approach keeps them unaltered.
Language: English
Topic: ECG Monitoring and Analysis
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W4411726883