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