Comparative analysis of different techniques for removal of baseline wander from Wrist Pulse Signals(WPS)

Wrist Pulse Signal (WPS) has significant diagnostic importance and is one of the oldest tool used by Ayurvedic physicians in Traditional Indian Medicine. The WPS acquisition is prone to the intervention from other biological and environmental sources. Baseline wandering can disguise some imperative features of the WPS hence it is a need to eradicate the noise for appropriate analysis and display of the WPS. The elimination of baseline wanders can cause misinterpretation of important diagnostic information as the frequency spectrum of baseline wander is generally superimposed with a low frequency component of WPS. The aim of this paper is to examine different advances for achieving baseline noise removal in the WPS by comparing different techniques. The techniques applied in this work are high pass filtering, wavelet filter and polynomial fitting. To validate the results,performance analysis was carried out by evaluating Mean Square Error (MSE) and Peak Signal To Noise Ratio (PSNR) in db. Our experimental results showed that the wavelet transform approach performs better than other techniques and eliminates base line divergence.