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Proceedings Paper

On exploiting interbeat correlation in compressive sensing-based 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 data acquisition scheme with the potential to reduce the number of measurements required by the Nyquist sampling theorem to acquire sparse signals. We recently used the interbeat correlation to find the common support between jointly sparse adjacent heartbeats. In this paper, we fully exploit this correlation to find the magnitude, in addition to the support of the significant coefficients in the sparse domain. The approach used for this purpose is based on sparse Bayesian learning algorithms due to its superior performance compared to other reconstruction algorithms and the fact that being a probabilistic approach facilitates the incorporation of correlation information. The reconstruction includes, in the first place, the detection of the R peaks and the length normalization of ECG cycles to take advantage of the quasi-periodic structure. Since the common support reduces as the number of heartbeats increases, we propose the use of a sliding window where the support maintains approximately constant across cycles. The sparse Bayesian algorithm adaptively learns and exploits the high correlation between the heartbeats in the constructed window. Experimental results show that the proposed method reduces significantly the number of measurements required to achieve good reconstruction quality, validating the potential of using correlation information in compressed sensing-based ECG compression.

Paper Details

Date Published: 8 June 2012
PDF: 7 pages
Proc. SPIE 8365, Compressive Sensing, 83650D (8 June 2012); doi: 10.1117/12.919437
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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