Adaptive baseline wander removal in the pulse waveform

Abstract

The pulse waveform plays an important role in pulse diagnosis, which is the key technique in traditional Chinese medicine. However, its baseline wander introduced in the acquisition process will result in misdiagnosis. Therefore a wavelet based cascade adaptive filter to remove this wander is presented. This cascade adaptive filter works in two stages. The first stage is a discrete Meyer wavelet filter and the second stage is the cubic spline estimation. Compared with some traditional methods, such as cubic spline estimation and linear-phase FIR least-squares error minimization digital filter, the proposed approach has better performance for removing the baseline wander of the pulse waveform.