4. Baseline Wander Correction in Pulse Waveforms Using Wavelet-Based Cascaded Adaptive Filter

Quantifying pulse diagnosis is to acquire and record pulse waveforms by a set of sensor firstly and then analyze these pulse waveforms. However, respiration and artifact motion during pulse waveform acquisition can introduce baseline drift. It is necessary, therefore, to remove the pulse waveform’s baseline drift in order to perform accurate pulse waveform analysis. This chapter presents a wavelet-based cascaded adaptive filter (CAF) to remove the baseline drift of pulse waveform. To evaluate the level of baseline drift, we introduce a criterion: energy ratio (ER) of pulse waveform to its baseline drift. If the ER is more than a given threshold, the baseline drift can be removed only by cubic spline estimation; otherwise it must be filtered by, in sequence, discrete Meyer wavelet filter and the cubic spline estimation. Compared with traditional methods such as cubic spline estimation, morphology filter, and linear-phase finite impulse response (FIR) least-squares-error digital filter, the experimental results on 50 simulated and 500 real pulse signals demonstrate the power of CAF filter both in removing baseline drift and in preserving the diagnostic information of pulse waveforms. This CAF also can be used to remove the baseline drift of other physiological signals, such as ECG and so on.