Improving electrocardiography signal quality: introducing an efficient approach for noise removal

To evaluate the heart's electrical activity, electrocardiography (ECG) is commonly utilised. However, power line interference, muscular artefacts, and baseline drift are only a few examples of noise that can affect the accuracy and reliability of ECG signals. An effective method for noise removal is introduced in this paper as a novel strategy for enhancing the quality of ECG signals. The suggested approach uses cutting-edge signal processing techniques and machine learning algorithms to isolate and eliminate unwanted noise without altering the original cardiac signal. Pre-processing, feature extraction, noise estimation, and adaptive filtering are the cornerstones of the methodology. Experimental results on various ECG recordings show that the proposed method is effective at drastically lowering noise interference and improving the quality of ECG signals overall. With higher signal quality, doctors may make more informed patient care decisions. The proposed method achieves the highest SNR of 4025 dB after filtering, indicating that it effectively reduces noise and enhances the quality of the signal by a significant margin compared to the other methods. There is promising potential for the presented approach to be included in preexisting ECG devices and systems, giving a realistic option for noise reduction in clinical situations.