Wavelet Integrated Convolutional Neural Network for ECG Signal Denoising

Takamasa Terada, Masahiro Toyoura
1/12/2025
eess.SPcs.CV

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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.

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Cite this paper

@article{terada2025wavelet,
  title  = {Wavelet Integrated Convolutional Neural Network for ECG Signal Denoising},
  author = {Takamasa Terada and Masahiro Toyoura},
  year   = {2025},
  eprint = {2501.06724},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2501.06724v1}
}

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