期刊
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
卷 60, 期 2, 页码 300-309出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TBME.2012.2226175
关键词
Block sparse Bayesian learning (BSBL); compressed sensing (CS); fetal ECG (FECG); healthcare; independent component analysis (ICA); telemedicine; telemonitoring
资金
- National Science Foundation [CCF-0830612, CCF-1144258, DGE-0333451]
- Army Research Laboratory
- Army Research Office
- Office of Naval Research
- Defense Advanced Research Projects Agency
- Division of Computing and Communication Foundations
- Direct For Computer & Info Scie & Enginr [1144258] Funding Source: National Science Foundation
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 nonsparsity and strong noise contamination, current CS algorithms generally fail in this application. This paper proposes to use the block sparse Bayesian learning framework to compress/reconstruct nonsparse 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 that 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.
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