A New ECG Denoising Framework Using Generative Adversarial Network.

Area of Science:

• Biomedical Engineering
• Signal Processing
• Artificial Intelligence

Background:

• Electrocardiogram (ECG) signal noise is a common issue in recordings.
• ECG denoising is crucial for accurate analysis and diagnosis.
• Existing denoising methods rely on time-domain decomposition, thresholding, and filtering.

Purpose of the Study:

• To propose a novel ECG denoising method using Generative Adversarial Networks (GANs).
• To evaluate the effectiveness of a Convolutional Neural Network (CNN) based GAN for ECG noise filtering.
• To compare the proposed GAN-based approach against traditional denoising techniques.

Main Methods:

• Development of a Convolutional Neural Network (CNN) based Generative Adversarial Network (GAN) model.
• End-to-end training of the GAN model using both clean and noisy ECG signals.
• Utilizing the MIT-BIH Arrhythmia database for comprehensive analysis.

Main Results:

• The proposed GAN-based method demonstrates improved performance in ECG denoising.
• The technique effectively filters noise from ECG signals.
• Qualitative and quantitative analyses confirm the denoising efficacy.

Conclusions:

• The novel GAN-based approach offers a promising alternative for ECG denoising.
• This work opens avenues for further research into GAN applications in ECG signal processing.
• The improved denoising performance can enhance the reliability of ECG-based diagnostics.