ECG Signal Denoising Using Wavelet Transform in MATLAB

In this technical implementation, we will perform ECG signal denoising using MATLAB's wavelet transform methodology. The process begins by importing ECG signal data from a provided TXT file using MATLAB's file reading functions such as `readtable()` or `load()`. Once the signal data is loaded into the MATLAB workspace, we apply wavelet denoising algorithms to eliminate noise components while preserving essential ECG characteristics. The implementation typically involves key steps: wavelet decomposition using functions like `wavedec()` to break down the signal into approximate and detail coefficients, thresholding of detail coefficients to remove noise components using `wthresh()` or `wden()`, and signal reconstruction through `waverec()`. This wavelet-based approach effectively separates noise from the actual cardiac signal components, significantly enhancing the signal-to-noise ratio. Through this denoising process, we obtain cleaner ECG signals that facilitate more accurate analysis of cardiac patterns and features. The noise reduction improves signal clarity and reliability, providing a more robust data foundation for subsequent cardiovascular research and diagnostic applications. The MATLAB wavelet method proves particularly effective for ECG denoising due to its ability to handle non-stationary signals and preserve important morphological features like QRS complexes, making it an essential tool in biomedical signal processing workflows.