METAHEURISTIC-DRIVEN MULTI OBJECTIVE FIR FILTER DESIGN FOR ECG DENOISING

Digital signal processing in biomedical applications requires advanced filtering techniques that can simultaneously optimize multiple performance criteria. This study introduces a novel multi-objective metaheuristic approach for Finite Impulse Response (FIR) filter design, addressing the complex challenges of signal processing optimization. The research employs advanced metaheuristic algorithms, including Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Genetic Algorithm (GA), Black Widow Optimization (BWO), Chaos Game Optimization (CGO), Harmony Search (HS), Squirrel Search Algorithm (SSA), and Atomic Orbital Search (AOS) to optimize multiple filter design parameters jointly. In contrast to conventional approaches, the proposed multi-objective optimization strategy demonstrates superior performance in signal filtering. The proposed method considers the important design trade-offs at the same time, such as minimizing signal power after stopband frequency, reducing stopband ripple, maximizing stopband first lobe and stopband edge frequency attenuation, and reducing computational complexity. The method is employed on ElectroCardioGram (ECG) signals from the MIT-BIH open-access database. Performance comparison indicated that multi-objective metaheuristic filters achieved much better performance than conventional FIR filters. The optimized filters exhibited improved stopband attenuation, narrower transition bands, lower power leakage, and more efficient signal processing at a cut-off frequency of 100 Hz. Signal power measurements demonstrated significant improvements. While conventional FIR filters ranged from-37.2001 dB to-41.46778 dB, multi-objective metaheuristic filters reached-42.05318 dB to-44.69498 dB in terms of stopband power. © 2025, XLESCIENCE. All rights reserved.