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.
Highlights
The processing of under-sampled signals has attracted significant research interest in the last decade [1,2,3,4,5,6,7,8,9,10]
The Gini index satisfies most of the desirable characteristics of measures of sparsity and overcomes the limitations of standard norm-based sparsity measures, as proven in [41]. It is suitable for comparing the sparsity of a signal in different transform domains [42], and is used as a measure of sparsity for biomedical signals [43,44]; this measure is implemented within the instrument together with the1 -norm concentration
This algorithm suits for a general class of signals, and for both 1D and 2D cases. It can be successfully used when the measurements are affected by the noise and provides satisfactory results for the natural images reconstruction from a very reduced set of pixels. Greedy approaches such as single iteration construction algorithm (SIRA), orthogonal matching pursuit (OMP), and generalized deviation-based reconstruction algorithm (GDBRA) are faster, but less precise compared with convex optimization algorithms and require a priori knowledge about the signal
Summary
The processing of under-sampled signals has attracted significant research interest in the last decade [1,2,3,4,5,6,7,8,9,10]. By providing the analytical visualization of results, together with reconstruction error and concentration measures, the tool allows users to test and choose the best possible optimization approach and the most suitable sparse domain for the applications with biomedical signals or images. On the basis of the statistical parameters for measuring reconstruction efficiency, the users are able to choose the most suitable among the offered solution It is convenient for the researchers working in the field, as it provides a set of comprehensive solutions for the processing of biomedical data that can be further extended or adapted for different purposes.
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