Adaptive Dictionary Reconstruction for Compressed Sensing of ECG Signals

IEEE Journal of Biomedical and Health Informatics
Darren CravenEdward Jones

Abstract

This paper proposes a novel adaptive dictionary (AD) reconstruction scheme to improve the performance of compressed sensing (CS) with electrocardiogram signals (ECG). The method is based on the use of multiple dictionaries, created using dictionary learning (DL) techniques for CS signal reconstruction. The modified reconstruction framework is a two-stage process that leverages information about the signal from an initial signal reconstruction stage. By identifying whether a QRS complex is present and if so, determining a location estimate of the QRS, the most appropriate dictionary is selected and a second stage more refined signal reconstruction can be obtained. The performance of the proposed algorithm is compared with state-of-the-art CS implementations in the literature, as well as the set partitioning in hierarchical trees (SPIHT) wavelet-based lossy compression algorithm. The results indicate that the proposed reconstruction scheme outperforms all existing CS implementations in terms of signal fidelity at each compression ratio tested. The performance of the proposed approach also compares favorably with SPIHT in terms of signal reconstruction quality. Furthermore, an analysis of the overall power consumption of the propo...Continue Reading

References

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May 25, 2011·IEEE Transactions on Bio-medical Engineering·Hossein MamaghanianPierre Vandergheynst
Jan 11, 2012·IEEE Transactions on Bio-medical Engineering·Tao MaHsiao-Hwa Chen
Sep 13, 2012·IEEE Transactions on Bio-medical Engineering·Zhilin ZhangBhaskar D Rao
Nov 13, 2012·IEEE Transactions on Bio-medical Engineering·Zhilin ZhangBhaskar D Rao
May 23, 2014·IEEE Journal of Biomedical and Health Informatics·Luisa PolaniaManuel Blanco-Velasco
Jun 1, 2014·IEEE Journal of Biomedical and Health Informatics·Darren CravenEdward Jones

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