Wavelet Integrated Convolutional Neural Network for ECG Signal Denoising

TL;DR

This paper introduces a wavelet-integrated CNN model for ECG signal denoising, effectively reducing noise in wearable ECGs with dry electrodes, especially under low SNR conditions.

Contribution

It presents a novel CNN architecture with a wavelet transform layer to enhance frequency feature extraction for improved ECG denoising.

Findings

Effective noise reduction in low SNR conditions

Higher efficiency compared to existing methods

Accurate ECG signal prediction with reduced noise

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.