Adaptive Electrocardiogram (ECG) Signal Denoising Using Particle Swarm Optimization-Based Non-Local Means (NLM)...

An ECG signal is usually corrupted by AWGN (additive white Gaussian noise), baseline wandering, power line interference (PLI), and motion artifacts, which may impact the reliable diagnosis of arrhythmias. This study proposes a hybrid ECG denoising framework that integrates Particle Swarm Optimization (PSO) with Non-Local Means (NLM) filtering to suppress diverse ECG artifacts while preserving signal morphology. In the first experiment, ECG signals from MIT-BIH Arrhythmia Database are artificially contaminated using AWGN and PLI to measure their denoising capability. In the second experiment, real-world baseline wander, muscle artifacts, and electrode motion artifacts from the MIT-BIH Noise Stress Test Database are used to evaluate the robustness of the proposed framework under practical noise conditions. The proposed method shows improved denoising performance in SNR, PRD, and RMSE for both experiments. Morphology preservation analysis represents minimal distortion of the waveforms and preservation of important ECG characteristics. Furthermore, a one-dimensional ResNet architecture is used for arrhythmia classification, demonstrating good classification performance on the denoised ECG signals with only a minor reduction compared with the noise-free signals. Overall, the proposed framework effectively suppresses diverse ECG artifacts while preserving diagnostically relevant information for arrhythmia classification.