ECG Signal Noise Processing Method Implementation

We have developed a comprehensive ECG signal noise processing method implemented in MATLAB. This method effectively removes various types of noise from ECG signals and significantly improves signal quality. The implementation follows a three-stage approach: First, we perform signal preprocessing through digital filtering techniques. This stage includes applying bandpass filters to remove high-frequency noise and low-frequency baseline wander. The MATLAB implementation typically uses built-in functions like 'designfilt' for creating digital filters and 'filtfilt' for zero-phase filtering to prevent phase distortion. Next, we employ specialized algorithms for noise component identification and elimination. The code implements techniques such as wavelet transform decomposition for detecting high-frequency artifacts and adaptive filtering methods for removing power line interference. Key functions include 'wden' for wavelet denoising and custom-written algorithms for motion artifact reduction using statistical thresholding. Finally, we conduct comprehensive analysis and quality assessment of the processed ECG signals. The implementation includes signal-to-noise ratio (SNR) calculations, template matching algorithms for waveform validation, and automated quality metrics using functions like 'corrcoef' for correlation analysis and custom visualization tools for signal comparison. This MATLAB-based methodology enables researchers to obtain cleaner ECG signals with enhanced feature preservation, providing reliable data for cardiac disease diagnosis and treatment studies. The modular code structure allows for easy customization of filter parameters and algorithm thresholds based on specific application requirements.