Enhancing Signal Quality: A Comparative Study of ICA and PCA in Denoising EMG and ECG Signals

Biomedical signal processing plays a pivotal role in extracting meaningful information from physiological signals, facilitating accurate diagnosis and treatment. In this study, we focus on the denoising of Electromyography (EMG) and Electrocardiography (ECG) signals, employing two prominent techniques: Independent Component Analysis (ICA) and Principal Component Analysis (PCA). Synthetic signals, representative of real-world scenarios, were subjected to these methods to assess their effectiveness in isolating underlying physiological components from noise.The denoising outcomes were evaluated in terms of their implications for muscle activity analysis in EMG signals and diagnostic accuracy in ECG signals. Our results reveal the distinct contributions of ICA and PCA, showcasing their potential utility in refining signal quality for diverse biomedical applications. This comparative study enhances our understanding of these denoising techniques, offering insights that can guide researchers and practitioners in choosing optimal approaches for specific signal processing challenges.