Adaptive Dictionary Reconstruction for Compressed Sensing of ECG Signals

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 reconstructi...

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Bibliographic Details
Published in:IEEE journal of biomedical and health informatics 2017-05, Vol.21 (3), p.645-654
Main Authors: Craven, Darren, McGinley, Brian, Kilmartin, Liam, Glavin, Martin, Jones, Edward
Format: Article
Language:English
Subjects:
Algorithms
Body area networks
Compressed sensing
Compressed sensing (CS)
Compression
Compression ratio
Dictionaries
dictionary learning (DL)
EKG
electrocardiogram (ECG) compression
Electrocardiography
Electrocardiography - methods
Humans
Information processing
Machine Learning
Power consumption
Signal processing
Signal Processing, Computer-Assisted
Signal reconstruction
Training
Wavelet analysis
wireless body area networks (BANs)
Wireless communication
Wireless sensor networks
Wireless Technology
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Summary: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 proposed ECG compression framework as would be used in a body area network (BAN) demonstrates positive results for the proposed CS approach when compared with existing CS techniques and SPIHT.
ISSN:2168-2194
2168-2208
DOI:10.1109/JBHI.2016.2531182