Paper
8 June 2012 On exploiting interbeat correlation in compressive sensing-based ECG compression
Luisa F. Polania, Rafael E. Carrillo, Manuel Blanco-Velasco, Kenneth E. Barner
Author Affiliations +
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.
© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Luisa F. Polania, Rafael E. Carrillo, Manuel Blanco-Velasco, and Kenneth E. Barner "On exploiting interbeat correlation in compressive sensing-based ECG compression", Proc. SPIE 8365, Compressive Sensing, 83650D (8 June 2012); https://doi.org/10.1117/12.919437
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Cited by 5 scholarly publications.
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KEYWORDS
Electrocardiography

Reconstruction algorithms

Wavelets

Chromium

Compressed sensing

Databases

Quantization

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