Redundant Dictionary based Pulse Wave Compressed Sensing

With the development of sensor technology and wireless communication technology, pulse wave based medical or healthcare instruments have become smaller, portable, and more intelligent. However, energy efficiency is still one of the obstacles for such devices in long term real-time use. Data compression can reduce energy wasting in communication. It is an efficient method for prolonging the lifetime of wireless pulse wave devices. Here, we present a redundant Gaussian dictionary (RGD) based compressed sensing (CS) framework for pulse signals compression. The dictionary is composed of atoms computed as samples of Gaussian function, and a set of parameters are given out for RGD generation. To verify the advantage of the proposed method, we compare the proposed RGD with discrete cosine transformation (DCT), wavelet transform (WT), fast Fourier transform (FFT), and Gabor dictionary. Meanwhile, experiments are done for the determination of the optimal combination of the proposed dictionary with the measurement matrix and reconstruction algorithm. Numerical experiments demonstrate that the RGD can increase the pulse wave reconstruction quality under a higher compression ratio combined with 1-bit measurement matrix and SL0 reconstruction algorithm, it achieves the best performance in MIMIC database testing, the pulse signals is well reconstructed with up to 82% data reduction.