Shape-Preserving Preprocessing for Human Pulse Signals Based on Adaptive Parameter Determination

Abstract

The use of the human pulse signal for medical diagnosis is a mainstay in the practice of traditional Chinese medicine. Computer processing of this signal may be used to automate diagnostic procedures and to reveal sources of information in the waveform that have been used by both eastern and western physicians for more than two millennia. A new method for preprocessing of the human pulse signal significantly improves feature extraction and classification of the waveform. Baseline distortion is first removed using the dual-tree complex wavelet transform (DT-CWT) and cubic spline interpolation, then a novel filtering method removes the residual background noise. Filtering is implemented in two stages. In the initial pass, a majority of the noise is eliminated by an adaptive mean filter whose sliding window duration is selected automatically based on a chain code and the DT-CWT. In the second pass, residual high frequency noise is removed using the DT-CWT with a new threshold determination. Experimental results demonstrate effective removal of background disturbances with excellent preservation of pulse peak information essential for proper parametric representation and classification of the waveform.