Wavelet Integrated Convolutional Neural Network for ECG Signal Denoising
This addresses noise reduction in wearable ECG monitoring for healthcare applications, but it appears incremental as it combines existing CNN and wavelet transform techniques.
The study tackled the problem of high-intensity noise in wearable ECG signals by proposing a CNN model with an integrated wavelet transform layer, which effectively denoised signals, showing higher efficiency at low SNR levels (e.g., -10 to 10 range).
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.