Implementation of Compressed Sensing for fPCG Signal Compression: design and performance evaluation

In this article we innovate and develop the ShuffledCompressed Sensing (S-CS) method which provides the efficient compression and reconstruction of fPCG recorded signals. As the fPCG signal is not spares in time domain it should be brought to another orthogonal space. Because of the structured sparsity of fPCG coefficients in the orthogonal space, the CS method should be adapted to the fPCG signal’s features. Thus to improve the compression performance, we used a shuffler matrix to defuse the structured sparsity. Then, the reconstruction problem would be a convex optimization problem which can be solved by recursive and iterative algorithms. The simulation results shows that the proposed S-CS method is a beneficial principle for the fPCG signal compression with the acceptable reconstruction errors.