Sparse Analyzer Tool for Biomedical Signals

Abstract

The virtual (software) instrument with a statistical analyzer for testing algorithms for biomedical signals’ recovery in compressive sensing (CS) scenario is presented. Various CS reconstruction algorithms are implemented with the aim to be applicable for different types of biomedical signals and different applications with under-sampled data. Incomplete sampling/sensing can be considered as a sort of signal damage, where missing data can occur as a result of noise or the incomplete signal acquisition procedure. Many approaches for recovering the missing signal parts have been developed, depending on the signal nature. Here, several approaches and their applications are presented for medical signals and images. The possibility to analyze results using different statistical parameters is provided, with the aim to choose the most suitable approach for a specific application. The instrument provides manifold possibilities such as fitting different parameters for the considered signal and testing the efficiency under different percentages of missing data. The reconstruction accuracy is measured by the mean square error (MSE) between original and reconstructed signal. Computational time is important from the aspect of power requirements, thus enabling the selection of a suitable algorithm. The instrument contains its own signal database, but there is also the possibility to load any external data for analysis.

Photoacoustic image improvement based on a combination of sparse coding and filtering

“…Recently, the TV approach that promotes the sparsity of signals in the gradient domain has attracted significant attention in signal denoising applications. 48 The goal of TV denoising technique is to efficiently estimate and recover the desired N-point signal S ¼ fsðnÞg N n¼1 with the sparse or sparse-derivative representation from the measured noisy signal x, which is defined as E Q -T A R G E T ; t e m p : i n t r a l i n k -; e 0 0 1 ; 1 1 6 ; 5 3 9…”

“…Recently, the TV approach that promotes the sparsity of signals in the gradient domain has attracted significant attention in signal denoising applications. 48 The goal of TV denoising technique is to efficiently estimate and recover the desired -point signal with the sparse or sparse-derivative representation from the measured noisy signal , which is defined as where is considered as additive Gaussian noise with the variance of . TV denoising could be defined as the constrained minimization problem of a non-differentiable cost function in terms of the norm as below: where as the first-order difference matrix is of size and is the first-order difference of an -point signal .…”