Compressed Sensing for Energy-Efficient Wireless Telemonitoring of Noninvasive Fetal ECG Via Block Sparse Bayesian Learning

By Zhilin Zhang, Tzyy-Ping Jung, Scott Makeig, Bhaskar D. Rao. IEEE Transactions on Biomedical Engineering, 2012

335 citations recorded by the source corpus.

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.

Subfields: Biomedical Engineering, Computational Mechanics, Signal Processing

View Paper: https://www.semanticscholar.org/paper/6c24d88584e613c0b8f81491e4d410d2465d45a4

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Citations in the corpus

Extension of SBL Algorithms for the Recovery of Block Sparse Signals With Intra-Block Correlation (2012, background): “Experiments on real-world data can be found in [10].”
Smart e-Health Gateway: Bringing intelligence to Internet-of-Things based ubiquitous healthcare systems (2015, background): “3) Data Filtering: Physiological systems of the human body such as cardiovascular, nervous and muscular systems generate bio-signals that are the primary source of information for assessing the patient health status.”
A Survey on Wireless Body Area Networks: Technologies and Design Challenges (2014, methodology): “In [84] the authors show how to use block sparse Bayesian learning to reconstruct a sub-Nyquist sampled signal (fetal ECG) exploiting its correlation, and they proved the effectiveness of their approach with experimental results.”
ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive Sensing (2017, methodology): “CS has been applied in many practical applications, including but not limited to singlepixel imaging [11, 33], accelerating magnetic resonance imaging (MRI) [26], wireless tele-monitoring [50] and cognitive radio communication [36].”
Intelligent Bearing Fault Diagnosis Method Combining Compressed Data Acquisition and Deep Learning (2018, neutral): “gram [28].”