Structured Sparsity Models for Compressively Sensed Electrocardiogram Signals: A Comparative Study

We have recently quantified and validated the potential of the emerging compressed sensing (CS) paradigm for real-time energy-efficient electrocardiogram (ECG) compression on resource-constrained sensors. In the present work, we investigate applying sparsity models to exploit underlying structural information in recovery algorithms. More specifically, re-visiting well-known sparse recovery algorithms, we propose novel model- based adaptations for the robust recovery of compressible signals like ECG. Our results show significant performance gains for the recovery algorithms exploiting the underlying sparsity models.


Presented at:
Biomedical Circuits and Systems Conference (BioCAS), 2011 IEEE, San Diego, CA, USA, November 10-12, 2011
Year:
2011
Laboratories:




 Record created 2014-04-13, last modified 2018-09-13

n/a:
Download fulltext
PDF

Rate this document:

Rate this document:
1
2
3
 
(Not yet reviewed)