Pulse baseline wander removal using wavelet approximation

Pulse waveform is the key to the traditional Chinese pulse diagnosis. However, its baseline wander introduced in the acquisition may result in misdiagnosis. What´s more, recent advancements in the pulse variability analysis require more accuracy of baseline estimation. In this paper, a wavelet based cascaded adaptive filter (CAF) was presented to remove this drift. The CAF works in two stages. The first stage is a discrete Meyer wavelet approximation and the second stage is a cubic spline estimation. The experimental results on 50 simulated and 200 real pulse signals demonstrate the powerful effect of CAF both in removing the baseline wander and in preserving the diagnostic information of pulse waveform, comparing with some traditional methods, such as cubic spline estimation, morphology filter and linear-phase FIR least-squares-error digital filter. In addition, this CAF is easy to be accomplished and needs no prior knowledge on pulse and its baseline drift.