Performance Analysis of FIR Adaptive Filter Algorithms for Denoising Adult ECG Signals: A Comparative Study

Abstract

This paper describes a unique study that uses multiple FIR adaptive filter algorithms to denoise adult electrocardiogram (ECG) data. The study looks at how power line interference, external electromagnetic fields, random body motions, and breathing impact ECG measurement accuracy. The article takes a fresh look at Savitzky-Golay filtering techniques by implementing and evaluating them inside the FIR adaptive filter architecture. Matlab is used to evaluate the performance of the Affine projection FIR adaptive filter (AP), Direct-form Normalized least-mean-square FIR adaptive filter (NLMS), and Sliding-window Recursive least-squares FIR adaptive filter (SWRLS). The results show how different strategies compare in terms of performance and their influence on recorded waveform quality. The study extends to our understanding of the efficiency of FIR adaptive filter algorithms in decreasing ECG signal noise and helps us better understand their potential uses in ECG signal processing. Based on reliable ECG data, the research findings assist the development of new approaches for diagnosing aberrant cardiac rhythms and examining the origins of chest discomfort. The originality of this work comes in its thorough assessment, comparison, and unique use of Savitzky-Golay filtering techniques inside FIR adaptive filter algorithms, which contributes to the area of ECG signal denoising. According to a comparative investigation, the SWRLS FIR adaptive filter method improves ECG signal denoising by 91.53% noise reduction.