Wavelet Integrated Convolutional Neural Network for ECG Signal Denoising

• URL: http://arxiv.org/abs/2501.06724v1
• Date: Sun, 12 Jan 2025 06:18:46 GMT
• Title: Wavelet Integrated Convolutional Neural Network for ECG Signal Denoising
• Authors: Takamasa Terada, Masahiro Toyoura,
• Abstract summary: Wearable electrocardiogram (ECG) measurement using dry electrodes has a problem with high-intensity noise distortion.<n>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.<n>Testing confirms that the proposed method effectively predicts accurate ECG behavior with reduced noise by accounting for all frequency domains.
• Score: 1.565361244756411
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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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