Noise Removal from ECG Signal using LMS Adaptive Filter Implementation in Xilinx System Generator

• Published in: 2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC) pp. 1488 - 1491
• Main Authors: Vaishnavi, A. Sree, Greeshma, T, Teja, Ram Prudhvi, Padma, Tatiparti, Kumari, Ch Usha
• Format: Conference Proceeding
• Language: English
• Published: IEEE 04.05.2023
• Subjects: Adaptation models; Adaptive filters; Adaptive noise; Convergence rate; Electrocardiography; Filtering; Filtering algorithms; Finite impulse response filters; Least Mean Square; Mean square error (MSE); Software packages
• DOI: 10.1109/ICAAIC56838.2023.10140865

The paper provides an overview of the removal of noise cancellation in ECG signals using an LMS filter in a system generator for monitoring ECG parameters and the study of the P wave to diagnose cardiac arrhythmia. The real ECG signals were evaluated from MIT-BIH database. Using Xilinx system Generator., the LMS adaptive filters technique is implemented. In order to efficiently verify the algorithm., the simulation of the models was carried out in MATLAB and Simulink. The core LMS adaptive filter and its fundamental basic building blocks technique was implemented in Xilinx System Generator. Here., high-pass least-square linear phase Finite Impulse Response (FIR) filtering approach to remove the baseline wander noise from the system's input ECG signal. A digital filter used in adaptive filtering has weights that are managed using adaptive algorithm to reduce difference between output of the filter and a reference signal that matches and fulfills the criterion. The reference signal's characteristics depends on the application under consideration. Convergence rate and steady state mean square error are the two main measures to evaluate the efficiency and performance of an adaptive filter.