Key result
A multistage cascaded LMS adaptive FIR filter effectively suppressed powerline interference, muscle noise, and baseline wander in synthetic and real-world ECG datasets.
Why the study?
ECG signals are often corrupted by artifacts such as powerline interference, muscle noise, and baseline wander, which can obscure important clinical information.
A novel multistage cascaded LMS adaptive FIR filter effectively removes multiple artifacts from ECG signals, potentially improving the precision of cardiac monitoring.
May enhance ECG denoising; leaves open prospective clinical validation before practice adoption.
Electrocardiogram (ECG) signals are vital in diagnosing various cardiac abnormalities, yet they are often corrupted by artifacts such as powerline interference (PLI), muscle noise, and baseline wander (BW), which can obscure important clinical information. The present research explores a novel approach for multiple artifacts removal from ECG signals using cascading of multistage least mean square (LMS) adaptive FIR filter. The proposed method employs a cascade of LMS adaptive FIR filters, each optimized for targeting specific artifacts found in the ECG signal. By iteratively adapting filter coefficients based on the input signal and a reference signal obtained from the preceding stage, the proposed cascaded LMS adaptive FIR filter effectively suppresses different artifacts while preserving the integrity of the ECG signal. The proposed algorithm’s performance has been analyzed by evaluating the mean square error (MSE) value and signal-to-noise ratio (SNR) of the filtered ECG, and evaluation conducted on synthetic and real-world ECG datasets demonstrates the efficacy of the proposed method in reducing various types of artifacts, thereby reliability of ECG signal interpretation and enhancing the quality. This method holds promise for improving the precision of cardiac monitoring and diagnosis in clinical settings. The proposed model is simulated on MATLAB to measure the performance parameter which demonstrates the validation of model.
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Yadav et al. (2025) studied ECG artifacts (powerline interference, muscle noise, baseline wander). Multistage cascaded LMS adaptive FIR filter was evaluated on Mean square error (MSE) and signal-to-noise ratio (SNR). A multistage cascaded LMS adaptive FIR filter effectively suppressed powerline interference, muscle noise, and baseline wander in synthetic and real-world ECG datasets.
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