Fast and efficient signals recovery for deterministic compressive sensing: Applications to biosignals

Abstract

Compressed sensing is a technique that is suitable for compressing and recovering signals having sparse representations in certain bases. Compressed sensing has been widely used to optimize the measurement process of power and bandwidth constrained systems like wireless body sensor network. The central issues with compressed sensing are mainly the construction of measurement matrices and the development of efficient recovery algorithms. In this paper, we proposed a simple and fast recovery algorithm which performed a thresholding in the discrete cosine transform domain. We validated it by recovering electrocardiogram and electromyogram signals taken from the Phyiobank database. The simulation and experimental results have shown that the proposed recovery algorithm was 25 and 12 times faster than orthogonal matching pursuit and stagewise orthogonal matching pursuit, respectively. In addition, depending on the compression ratio, the signal-to-noise ratio of recovered signals were improved up to 2 dB.

A Method of Constructing Measurement Matrix for Compressed Sensing by Chebyshev Chaotic Sequence

“…Gaussian random matrix [ 7 ] and Bernoulli random matrix [ 8 ] are frequently used because they satisfy the RIP and are uncorrelated with most sparse domains. They guarantee good reconstruction performance but bring a specific challenge to hardware such as the requirement for storage space and system design [ 9 ]. On the contrary, deterministic measurement matrices have the advantages not only of economic storage space but also of convenience in engineering design.…”