Hybrid DWT NLM method with NOA optimization for ECG signal denoising

Abstract Despite the hybrid Discrete Wavelet Transform+Non Local Mean (DWT+NLM) method’s ability to leverage the processing strengths of high - and low - frequency components, it faces issues like translation transformations, modal aliasing, patch effects, and threshold - induced distortion. These problems undermine the accuracy of electrocardiogram (ECG) - based cardiovascular disease diagnosis. This study presents a Nutcracker Optimization Algorithm (NOA) - enhanced DWT+NLM framework. Using NOA, the framework dynamically optimizes wavelet decomposition levels and basis functions for precise high/low - frequency separation. It adaptively adjusts NLM parameters to mitigate the patch effect and introduces a sigmoid - tuned threshold function to eliminate constant deviation. Experiments conducted on Physionet datasets demonstrate that when mitigating Additive White Gaussian Noise (AWGN), the proposed method achieves a maximum Signal-to-Noise Ratio (SNR) gain of 2.42 dB and an average gain of 1.73 dB over the suboptimal approach specifically for AWGN. Notably, in real-world noise scenarios (Baseline Wander (BW), Muscle Artifact (MA), and Electrode Motion Artifact (EM)), the method delivers an average SNR enhancement of 3.12 dB compared to the second-best method, underscoring its robust adaptability and practical superiority in noisy environments. This research holds promise for integration into wearable ECG sensors, potentially enhancing the diagnostic accuracy of cardiovascular diseases.

Abstract licence: CC BY-NC-ND

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