ECG Signal Enhancement using Selective Weight Update Adaptive Filtering Techniques for Telemetry Applications

Electrocardiogram (ECG) signals serve as crucial diagnostic tools in healthcare, yet their accuracy can be compromised by various artifacts. This study investigates the efficiency of adaptive filtering techniques in removing artifacts from ECG signals. Specifically, the research explores the application of Partial Update Least Mean Square (PULMS) algorithm, and its diverse variant Adaptive Weighted PULMS (AWPULMS) in the artifact removal. The PULMS algorithm, known for its adaptability and computational efficiency, offers promising results in mitigating artifacts within ECG signals. Sequential variations of this method enhance adaptivity by integrating incremental updates, providing refined noise cancellation capabilities. Furthermore, exploring different variants of the PULMS algorithm underscores its versatility and effectiveness in addressing various artifact patterns within ECG signals. Evaluation of these adaptive filtering techniques reveals significant advancements in artifact removal, particularly in telemetry applications. The PULMS-based adaptive noise cancellers exhibit superior performance in enhancing ECG signal quality, demonstrating their suitability for telemetry applications where accurate and reliable signal transmission is paramount. In conclusion, the utilization of AWPULMS-based adaptive noise cancellers emerges as a preferred approach for artifact removal in telemetry applications, offering enhanced ECG signal fidelity essential for precise clinical diagnostics and monitoring.