Fetal ECG (FECG) telemonitoring is an important branch in telemedicine. Thedesign of a telemonitoring system via a wireless body-area network with lowenergy consumption for ambulatory use is highly desirable. As an emergingtechnique, compressed sensing (CS) shows great promise incompressing/reconstructing data with low energy consumption. However, due tosome specific characteristics of raw FECG recordings such as non-sparsity andstrong noise contamination, current CS algorithms generally fail in thisapplication. This work proposes to use the block sparse Bayesian learning (BSBL) frameworkto compress/reconstruct non-sparse raw FECG recordings. Experimental resultsshow that the framework can reconstruct the raw recordings with high quality.Especially, the reconstruction does not destroy the interdependence relationamong the multichannel recordings. This ensures that the independent componentanalysis decomposition of the reconstructed recordings has high fidelity.Furthermore, the framework allows the use of a sparse binary sensing matrixwith much fewer nonzero entries to compress recordings. Particularly, eachcolumn of the matrix can contain only two nonzero entries. This shows theframework, compared to other algorithms such as current CS algorithms andwavelet algorithms, can greatly reduce code execution in CPU in the datacompression stage.
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