Low Energy Wireless Body-Area Networks for Fetal ECG Telemonitoring via the Framework of Block Sparse Bayesian Learning

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 FECG raw recordings such as non-sparsity and strong noise contamination, current CS algorithms generally fail for this application. This work proposes to use the block sparse Bayesian learning (bSBL) framework to compress/reconstruct non-sparse FECG raw recordings. Experiments show that the framework can reconstruct the recordings with high quality. Especially, the reconstruction does not destroy the interdependence relation among multichannel recordings, ensuring the independent component analysis (ICA) decomposition of the reconstructed recordings has high fidelity. Thus, the extracted FECGs using ICA from the reconstructed recordings have little distortion, satisfying requirements of clinical diagnosis. Besides, the framework allows to use a sparse binary sensing matrix with much less non-zero entries to compress recordings. Particularly, each column of the sensing matrix can contain only \emph{two} non-zero entries, implying the framework has almost achieved the lowest energy consumption in compression.