Hybrid DWT NLM method with NOA optimization for ECG signal denoising

Area of Science:

• Biomedical Engineering
• Signal Processing
• Cardiovascular Diagnostics

Background:

• Traditional Discrete Wavelet Transform+Non Local Mean (DWT+NLM) methods for electrocardiogram (ECG) signal processing suffer from translation variance, modal aliasing, patch effects, and threshold distortion.
• These artifacts compromise the accuracy of cardiovascular disease diagnosis from ECG data.

Purpose of the Study:

• To present a Nutcracker Optimization Algorithm (NOA) enhanced DWT+NLM framework for improved ECG signal denoising.
• To dynamically optimize wavelet decomposition and Non-Local Mean (NLM) parameters for enhanced noise reduction and artifact mitigation.

Main Methods:

• The proposed framework utilizes the Nutcracker Optimization Algorithm (NOA) to optimize wavelet decomposition levels and basis functions.
• Adaptive adjustment of NLM parameters and introduction of a sigmoid-tuned threshold function mitigate patch effects and constant deviation.
• Experiments were conducted on Physionet datasets, evaluating performance against Additive White Gaussian Noise (AWGN) and real-world noise (Baseline Wander, Muscle Artifact, Electrode Motion Artifact).

Main Results:

• The NOA-enhanced DWT+NLM method achieved a maximum Signal-to-Noise Ratio (SNR) gain of 2.42 dB for AWGN.
• In real-world noise scenarios, the method provided an average SNR enhancement of 3.12 dB over the second-best approach.
• Demonstrated robust adaptability and practical superiority in reducing various noise types in ECG signals.

Conclusions:

• The Nutcracker Optimization Algorithm (NOA) significantly improves the performance of DWT+NLM for ECG signal denoising.
• The enhanced framework offers robust noise reduction capabilities, crucial for accurate cardiovascular disease diagnosis.
• This method shows potential for integration into wearable ECG sensors to improve diagnostic accuracy.