The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Compressed Sensing for Energy-Efficient Wireless Telemonitoring of Noninvasive Fetal ECG Via Block Sparse Bayesian Learning
Open accessAbstract
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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Cites
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Block-Sparse Signals: Uncertainty Relations and Efficient Recovery
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Cited by
ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive Sensing
Zhang, J., & Ghanem, B. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00196 · Cited by 1,309
A Survey on Wireless Body Area Networks: Technologies and Design Challenges
Cavallari, R., Martelli, F., Rosini, R., Buratti, C., & Verdone, R. (2014) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2014.012214.00007 · Cited by 710
Extension of SBL Algorithms for the Recovery of Block Sparse Signals With Intra-Block Correlation
Zhang, Z., & Rao, B. D. (2013) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2013.2241055 · Cited by 558
Self-citationIntelligent Bearing Fault Diagnosis Method Combining Compressed Data Acquisition and Deep Learning
Sun, J., Yan, C., & Wen, J. (2018) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2017.2759418 · Cited by 422
ECG Monitoring Systems: Review, Architecture, Processes, and Key Challenges
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Metadata
- Authors
- Zhang, Z., Jung, T.-P., Makeig, S., & Rao, B. D.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2013-02
- Volume
- 60
- Issue
- 2
- Pages
- 300-309
- ISSN
- 0018-9294 (ISSN-L)
- 1558-2531
- OpenAlex
- W2111414067
- Cited by
- 340
- References
- 38
- FWCI
- 24.46
- Type
- journal article
- Language
- en
- Open access
- green
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- Zhang, Z., Jung, T.-P., Makeig, S., & Rao, B. D. (2013). Compressed Sensing for Energy-Efficient Wireless Telemonitoring of Noninvasive Fetal ECG Via Block Sparse Bayesian Learning. IEEE Transactions on Biomedical Engineering. https://doi.org/10.1109/tbme.2012.2226175
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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