  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.
