ABSTRACT
Due to its possible low-power implementation, Compressed
Sensing (CS) is an attractive tool for physiological signal
acquisition in emerging scenarios like Wireless Body Sensor
Networks (WBSN) and telemonitoring applications. In this
work we consider the continuous monitoring and analysis
of the fetal ECG signal (fECG). We propose a modification
of the low-complexity CS reconstruction SL0 algorithm,
improving its robustness in the presence of noisy original
signals and possibly ill-conditioned sensing/reconstruction
procedures. We show that, while maintaining the same computational
cost of the original algorithm, the proposed modification
significantly improves the reconstruction quality, both
for synthetic and real-world ECG signals. We also show that
the proposed algorithm allows robust heart beat classification
when sparse matrices, implementable with very low computational
complexity, are used for compressed sensing of the
ECG signal.
