Wavelet Transform Implementation for Surface Electromyography Signal Denoising

This program implements wavelet transform techniques to denoise surface electromyography (sEMG) signals, significantly enhancing signal quality and reliability. The implementation includes key functions for wavelet decomposition, threshold selection, and signal reconstruction using algorithms like discrete wavelet transform (DWT) with appropriate wavelet families (e.g., Daubechies, Symlets). We conducted comprehensive comparative analyses to evaluate the performance of wavelet transform methods against traditional denoising approaches such as frequency domain filtering and adaptive filtering. The technical exploration covers wavelet transform principles and applications, with comparisons to alternative denoising techniques through quantitative metrics like signal-to-noise ratio (SNR) improvement and mean squared error reduction. Our analysis concludes that wavelet transform methods demonstrate superior denoising performance, particularly when processing complex biological signals with non-stationary characteristics. This program provides valuable tools and references for researchers in biomedical signal processing, featuring MATLAB/Python implementations of wavelet decomposition levels optimization, thresholding strategies (soft/hard thresholding), and signal reconstruction algorithms for practical sEMG analysis applications.