Pulse wave signal modelling and feature extraction based on Lognormal function from photoplethysmography in wireless body area networks

Among the various physiological signals of the human body, the pulse wave signal from photoplethysmography is widely used for commercial products in Wireless Body Area Networks. The comfortable wearing of a product and the richness of physiological information are two advantages of Wireless Body Area Networks. To address the shortcomings of the existing modelling pulse wave signal in lacking characteristics that are consistent with a human physiological mechanism in medicine, we proposed a Lognormal function model of the pulse wave signal and a novel model to extract physiological parameters. The proposed Lognormal function model consists of four successively spaced single-peaked pulses with long trailing features. To define the parameters in the proposed Lognormal function model, we proposed a method that took advantage of the positivity and negativity of the first-order derivatives and the trans-zero point of the second-order derivatives. Based on the determined parameters, we introduced a four-shot staged curve fitting approach that can displace the sum of the four fits at iterative and different time scales. Finally, a parameter vector with 12 elements, which is known as a physiological feature to determine the health status of the human body in Wireless Body Area Networks. Experimental results show that the proposed Lognormal function model is superior to the conventional Gaussian function model in terms of physiological importance and waveform fitting accuracy.