Document Detail

Compressed Sensing for Energy-Efficient Wireless Telemonitoring of Non-Invasive Fetal ECG via Block Sparse Bayesian Learning.
MedLine Citation:
PMID:  23144028     Owner:  NLM     Status:  Publisher    
Fetal ECG (FECG) telemonitoring is an important branch in telemedicine. The design of a telemonitoring system via a wireless body-area network with low energy consumption for ambulatory use is highly desirable. As an emerging technique, compressed sensing (CS) shows great promise in compressing/ reconstructing data with low energy consumption. However, due to some specific characteristics of raw FECG recordings such as non-sparsity and strong noise contamination, current CS algorithms generally fail in this application. This work proposes to use the block sparse Bayesian learning (BSBL) framework to compress/reconstruct non-sparse raw FECG recordings. Experimental results show that the framework can reconstruct the raw recordings with high quality. Especially, the reconstruction does not destroy the interdependence relation among the multichannel recordings. This ensures that the independent component analysis decomposition of the reconstructed recordings has high fidelity. Furthermore, the framework allows the use of a sparse binary sensing matrix with much fewer nonzero entries to compress recordings. Particularly, each column of the matrix can contain only two nonzero entries. This shows the framework, compared to other algorithms such as current CS algorithms and wavelet algorithms, can greatly reduce code execution in CPU in the data compression stage.
Z Zhang; Tzyy-Ping Jung; S Makeig; B D Rao
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2012-10-23
Journal Detail:
Title:  IEEE transactions on bio-medical engineering     Volume:  -     ISSN:  1558-2531     ISO Abbreviation:  IEEE Trans Biomed Eng     Publication Date:  2012 Oct 
Date Detail:
Created Date:  2012-11-12     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  0012737     Medline TA:  IEEE Trans Biomed Eng     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
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