RLS adaptive filter co-design for de-noising ECG signal

Mahmood, Ahlam Fadhil; Awny, Safaa N.; Alameer, Ali

Abstract

Doctors diagnose various heart muscle disorders by continuously analyzing ELECTROCARDIOGRAM (ECG) signals. Obtaining a noise-free ECG recording is difficult due to various types of interference, making an effective filter essential for accurate diagnosis. This paper introduces a novel, low-complexity filter designed to enhance ECG signal quality. The proposed method involves partitioning the implementation of the Recursive Least Squares (RLS) adaptive filter between a Microblaze soft processor and hardware resources within a Field Programmable Gate Array (FPGA). The hardware component is responsible for creating a Finite Impulse Response (FIR) filter, while the adaptive processing is handled by the soft processor. This configuration makes the filter adaptable, allowing it to work with various algorithms for a wide range of applications. The co-design was tested for ECG noise removal, achieving an average Signal-to-Noise Ratio (SNR) improvement of 89.78 %. Offloading adaptive tasks to the soft processor reduced power consumption by 56.2 %, making it suitable for integration with ECG sensors in wearable body networks.

• Journal Article Type: Article
• Acceptance Date: Nov 26, 2024
• Publication Date: Dec 3, 2024
• Deposit Date: Mar 14, 2025
• Publicly Available Date: Mar 17, 2025
• Journal: Results in Engineering
• Print ISSN: 2590-1230
• Electronic ISSN: 2590-1230
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 24
• Article Number: 103563
• DOI: https://doi.org/10.1016/j.rineng.2024.103563
• Additional Information: This article is maintained by: Elsevier; Article Title: RLS adaptive filter co-design for de-noising ECG signal; Journal Title: Results in Engineering; CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.rineng.2024.103563; Content Type: article; Copyright: © 2024 The Authors. Published by Elsevier B.V.