Exploiting Prior Knowledge in Compressed Sensing Wireless ECG Systems

Recent results in telecardiology show that compressed sensing (CS) is a promising tool to lower energy consumption in wireless body area networks for electrocardiogram (ECG) monitoring. However, the performance of current CS-based algorithms, in terms of compression rate and reconstruction quality o...

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Bibliographic Details
Published in:IEEE journal of biomedical and health informatics 2015-03, Vol.19 (2), p.508-519
Main Authors: Polania, Luisa F., Carrillo, Rafael E., Blanco-Velasco, Manuel, Barner, Kenneth E.
Format: Article
Language:English
Subjects:
Algorithms
Approximation algorithms
Approximation methods
Compressed sensing
Data Compression - methods
Databases, Factual
Electrocardiography
Electrocardiography - methods
Humans
Remote Sensing Technology
Vectors
Wavelet Analysis
Wavelet transforms
Wireless communication
Wireless Technology
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Summary:Recent results in telecardiology show that compressed sensing (CS) is a promising tool to lower energy consumption in wireless body area networks for electrocardiogram (ECG) monitoring. However, the performance of current CS-based algorithms, in terms of compression rate and reconstruction quality of the ECG, still falls short of the performance attained by state-of-the-art wavelet-based algorithms. In this paper, we propose to exploit the structure of the wavelet representation of the ECG signal to boost the performance of CS-based methods for compression and reconstruction of ECG signals. More precisely, we incorporate prior information about the wavelet dependencies across scales into the reconstruction algorithms and exploit the high fraction of common support of the wavelet coefficients of consecutive ECG segments. Experimental results utilizing the MIT-BIH Arrhythmia Database show that significant performance gains, in terms of compression rate and reconstruction quality, can be obtained by the proposed algorithms compared to current CS-based methods.
ISSN:2168-2194
2168-2208
DOI:10.1109/JBHI.2014.2325017