Wavelet Integrated Convolutional Neural Network for ECG Signal Denoising
Abstract
Wearable electrocardiogram (ECG) measurement using dry electrodes has a problem with high-intensity noise distortion. Hence, a robust noise reduction method is required. However, overlapping frequency bands of ECG and noise make noise reduction difficult. Hence, it is necessary to provide a mechanism that changes the characteristics of the noise based on its intensity and type. This study proposes a convolutional neural network (CNN) model with an additional wavelet transform layer that extracts the specific frequency features in a clean ECG. Testing confirms that the proposed method effectively predicts accurate ECG behavior with reduced noise by accounting for all frequency domains. In an experiment, noisy signals in the signal-to-noise ratio (SNR) range of -10-10 are evaluated, demonstrating that the efficiency of the proposed method is higher when the SNR is small.
Cite
@article{arxiv.2501.06724,
title = {Wavelet Integrated Convolutional Neural Network for ECG Signal Denoising},
author = {Takamasa Terada and Masahiro Toyoura},
journal= {arXiv preprint arXiv:2501.06724},
year = {2025}
}