Enhanced ECG signal denoising using hybrid F2IR-IIR-DWT filter banks: A comprehensive approach

Electrocardiography (ECG) records the heart’s electrical activity, capturing vital cardiac functions for clinical analysis. However, external interferences often distort ECG signals, necessitating robust noise reduction techniques for enhanced signal quality. This study focuses on optimizing ECG signal fidelity through advanced filtering methods. Recent advancements in signal processing have enabled the removal of artifacts from ECG signals. The study proposes a comprehensive analysis of efficient noise reduction techniques, including IIR (Infinite Impulse Response), FIR(Finite Impulse Response), and DWT (Discrete Wavelet Transformation) filters. These filters are implemented with a soft approach to mitigate noise effectively. Evaluation metrics such as RMSE (Root Mean Square Error), PRD (Percentage Residual Difference), and SNR(Signal-to-Noise Ratio) are employed to assess the performance of the filters. The analysis utilizes Python 3.9 (Anaconda3) software and both raw ECG signals from portable healthcare devices and the standard MIT–BIH database for validation. Observational results demonstrate the efficacy of the proposed method in eliminating noise from ECG signals compared to existing techniques. By systematically comparing various parameters and metrics, the study elucidates the superiority of the implemented filters in enhancing signal quality. This research contributes to the ongoing efforts to develop robust noise reduction strategies for accurate ECG interpretation, thereby advancing clinical diagnostics and patient monitoring.