Integrated Zero-Phase and LMS Adaptive Filtering for Improving Heartbeat Signal Processing

This paper presents a novel hybrid framework that integrates Zero-Phase filtering (ZP) with adaptive Least Mean Squares (LMS) filtering to enhance noise reduction in signal generation and processing applications. The study focuses on generating and improving human heart rate monitor signals by effectively reducing noise, particularly under nonstationary conditions, through the combined advantages of ZP filtering’s distortion-free characteristics and the LMS algorithm’s adaptive capabilities. The proposed approach first applies ZP filtering to maintain the signal’s inherent features without introducing phase distortion, followed by adaptive LMS filtering to respond dynamically to varying noise conditions. Experimental tests on diverse non-stationary noise datasets reveal that this integrated method significantly outperforms individual filtering techniques in both noise suppression and signal fidelity. The findings demonstrate that the hybrid framework not only achieves superior noise reduction, closely simulating an electrocardiogram signal (ECG), but also preserves signal integrity, making it well-suited for world-time biomedical signal processing applications. This work introduces an innovative strategy that unites static and adaptive filtering techniques to address challenges posed by complex and random noise environments.