Electrocardiogram Signal Noise Reduction Application Employing Different Adaptive Filtering Algorithms

Abstract

Almost all signals existing in the universe experience varying degrees of noise interference. Specifically, audio signals necessitate efficient noise cancellation for most hearing devices to comfort the user. Various filtering techniques are employed in order to apply efficient noise cancellation, empowering the system to enhance the signal-to-noise ratio. Currently, adaptive filters are preferred to other types of filters to approach higher efficiency. This study presents and examines four adaptive filter algorithms, including least-mean-square, normalized least-mean-square, recursive-least-square, and Wiener filter. The selected models are simulated, benchmarked, and contrasted in some characteristics of the performance. The presented filters are applied to four different experiments/environments to further examine their functionality. All of that is performed utilizing different step sizes to monitor two compromised result parameters: performance and execution time. Eventually, the best adaptive filter possessing the optimal parameters and step size is acquired for electrocardiogram signals enabling physicians and health professionals to deal with electrocardiogram signals efficiently, empowering them to accurately and quickly diagnose any sign of heart problems. Simulation results further designate the superiority of the presented models.