Generative Adversarial Networks for Noise Reduction in Electrocardiogram Signals

Electrocardiogram (ECG) signals are essential for detecting and diagnosing heart diseases. Nevertheless, in the presence of noise and artefacts, ECG signals usually corrupt, hindering diagnostic and analysis. Conventional ECG denoising methods, like filtering techniques, are not efficient in mitigating complex noise patterns. This research examines the potential of applying Denoising based on GANs to ECG signals. ECG signals can be considered as time series signals. Deep ‘Recurrent Neural Networks’ were widely used for advanced regression and forecasting tasks, but that was before 2005 when Generative Adversarial Networks (GANs) were first described. We propose a GAN-based architecture for denoising ECG signals, and through experiments, we demonstrate its efficacy in removing both white noise and motion artefacts from ECG data. Our results indicate that GANs can outperform conventional denoising methods, leading to clearer and more reliable ECG signals for further clinical analysis.