Publication

Structured Sparsity Models for Compressively Sensed Electrocardiogram Signals: A Comparative Study

Pierre Vandergheynst
2011
Conference Papers

Abstract

We have recently quantified and validated the potential of the emerging compressed sensing (CS) paradigm for real-time energy-efficient electrocardiogram (ECG) compression on resource-constrained sensors. In the present work, we investigate applying sparsity models to exploit underlying structural information in recovery algorithms. More specifically, re-visiting well-known sparse recovery algorithms, we propose novel model- based adaptations for the robust recovery of compressible signals like ECG. Our results show significant performance gains for the recovery algorithms exploiting the underlying sparsity models.

Official source

https://infoscience.epfl.ch/entities/publication/479a8dea-9413-404c-821b-4a2c133adef5

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Pierre Vandergheynst
2011
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/479a8dea-9413-404c-821b-4a2c133adef5

About this result

Related concepts (22)

Compressed sensing

Compressed sensing (also known as compressive sensing, compressive sampling, or sparse sampling) is a signal processing technique for efficiently acquiring and reconstructing a signal, by finding solutions to underdetermined linear systems. This is based on the principle that, through optimization, the sparsity of a signal can be exploited to recover it from far fewer samples than required by the Nyquist–Shannon sampling theorem. There are two conditions under which recovery is possible.

Sparse approximation

Sparse approximation (also known as sparse representation) theory deals with sparse solutions for systems of linear equations. Techniques for finding these solutions and exploiting them in applications have found wide use in , signal processing, machine learning, medical imaging, and more. Consider a linear system of equations , where is an underdetermined matrix and . The matrix (typically assumed to be full-rank) is referred to as the dictionary, and is a signal of interest.

Sparse dictionary learning

Sparse dictionary learning (also known as sparse coding or SDL) is a representation learning method which aims at finding a sparse representation of the input data in the form of a linear combination of basic elements as well as those basic elements themselves. These elements are called atoms and they compose a dictionary. Atoms in the dictionary are not required to be orthogonal, and they may be an over-complete spanning set. This problem setup also allows the dimensionality of the signals being represented to be higher than the one of the signals being observed.

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Structured Sparsity Models for Compressively Sensed Electrocardiogram Signals: A Comparative Study

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