Removing Baseline Drift in Pulse Waveforms by a Wavelet Adaptive Filter

This work designs a wavelet adaptive filter (WAF) to remove the baseline drift from pulse waveforms. The WAF consists of two parts: the transform algorithm based on discrete Meyer wavelet to decompose the pulse signal into eight frequency bands; the improved adaptive filter that uses the high-frequency components of the pulse signal as reference input and the original pulse waveform added baseline drift as primary input. The WAF is tested on our developed pulse diagnosis apparatus. The results both on simulated and real human pulse signals demonstrate that the proposed WAF outperforms traditional filters not only in removing baseline drift but in preserving the diagnostic information of pulse waveforms.