Wavelet Filtering for ECG and Pulse Wave Signals

Wavelet filtering is a signal processing method particularly effective for filtering electrocardiogram (ECG) and pulse wave signals. This technique decomposes signals into different frequency components at multiple scales using wavelet transforms, allowing selective removal or retention of specific frequency bands. The implementation typically involves choosing appropriate wavelet families (like Daubechies or Symlets) and decomposition levels based on signal characteristics. Key steps include: signal decomposition using discrete wavelet transform (DWT), thresholding coefficients to remove noise components (using soft or hard thresholding methods), and signal reconstruction. This approach effectively reduces baseline wander, powerline interference, and motion artifacts while preserving important morphological features like QRS complexes in ECG or systolic peaks in pulse waves. Wavelet filtering is widely applied in medical signal processing and plays a crucial role in cardiac disease analysis, pulse wave analysis, and physiological monitoring systems. Code implementations often utilize libraries like PyWavelets or MATLAB's Wavelet Toolbox, with critical functions including wavedec for decomposition, wthresh for thresholding, and waverec for reconstruction.