Pulse Wave Extraction and Wavelet Transform Processing

Following pulse wave extraction, we can employ wavelet transform processing on the data to achieve baseline removal. This processing method enables further analysis of pulse wave characteristics and extraction of valuable information. Wavelet transform serves as an effective signal processing technique that decomposes signals into frequency components at different scales, providing deeper insights into signal variations and properties. Implementation typically involves selecting appropriate wavelet families (e.g., Daubechies, Symlets) and decomposition levels, followed by thresholding detail coefficients and reconstructing the signal without baseline components. Through wavelet-based processing of pulse wave data, we gain enhanced understanding of pulse wave characteristics, thereby expanding our research capabilities in biomedical signal analysis. The algorithm workflow generally includes: signal preprocessing, wavelet decomposition, coefficient thresholding, and signal reconstruction stages.