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Proceedings Paper

Compressive sensing exploiting wavelet-domain dependencies for ECG compression
Author(s): Luisa F. Polania; Rafael E. Carrillo; Manuel Blanco-Velasco; Kenneth E. Barner
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Paper Abstract

Compressive sensing (CS) is an emerging signal processing paradigm that enables sub-Nyquist sampling of sparse signals. Extensive previous work has exploited the sparse representation of ECG signals in compression applications. In this paper, we propose the use of wavelet domain dependencies to further reduce the number of samples in compressive sensing-based ECG compression while decreasing the computational complexity. R wave events manifest themselves as chains of large coefficients propagating across scales to form a connected subtree of the wavelet coefficient tree. We show that the incorporation of this connectedness as additional prior information into a modified version of the CoSaMP algorithm can significantly reduce the required number of samples to achieve good quality in the reconstruction. This approach also allows more control over the ECG signal reconstruction, in particular, the QRS complex, which is typically distorted when prior information is not included in the recovery. The compression algorithm was tested upon records selected from the MIT-BIH arrhythmia database. Simulation results show that the proposed algorithm leads to high compression ratios associated with low distortion levels relative to state-of-the-art compression algorithms.

Paper Details

Date Published: 8 June 2012
PDF: 9 pages
Proc. SPIE 8365, Compressive Sensing, 83650E (8 June 2012); doi: 10.1117/12.919478
Show Author Affiliations
Luisa F. Polania, Univ. of Delaware (United States)
Rafael E. Carrillo, École Polytechnique Fédérale de Lausanne (Switzerland)
Manuel Blanco-Velasco, Univ. de Alcalá (Spain)
Kenneth E. Barner, Univ. of Delaware (United States)


Published in SPIE Proceedings Vol. 8365:
Compressive Sensing
Fauzia Ahmad, Editor(s)

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