Energy and Quality Evaluation for Compressive Sensing of Fetal Electrocardiogram Signals

Energy and Quality Evaluation for Compressive Sensing of Fetal Electrocardiogram Signals

This manuscript addresses the problem of non-invasive fetal Electrocardiogram (ECG) signal acquisition with low power/low complexity sensors. A sensor architecture using the Compressive Sensing (CS) paradigm is compared to a standard compression scheme using wavelets in terms of energy consumption vs. reconstruction quality, and, more importantly, vs. performance of fetal heart beat detection i...

Improvement of the Fetal Electrocardiogram Signals

-The health of the fetus is of major concern to both obstetricians and expectant mothers. A non-invasive fetal electrocardiogram (FECG) system can monitor fetal cardiac condition during pregnancy. However, the FECG signal is often very weak or drowned out by the mother’s ECG (MECG) and other noises. This study uses a FastICA algorithm to the isolate FECG signal from other noise interference. Th...

Fast Greedy Approaches for Compressive Sensing of Large-Scale Signals

Cost-efficient compressive sensing is challenging when facing large-scale data, i.e., data with large sizes. Conventional compressive sensing methods for large-scale data will suffer from low computational efficiency and massive memory storage. In this paper, we revisit well-known solvers called greedy algorithms, including Orthogonal Matching Pursuit (OMP), Subspace Pursuit (SP), Orthogonal Ma...

Compressive Sensing system for recording of ECoG signals in-vivo

Compressive Sensing for Cluster Structured Sparse Signals: Variational Bayes Approach

Compressive Sensing (CS) provides a new paradigm of sub-Nyquist sampling which can be considered as an alternative to Nyquist sampling theorem. In particular, providing that signals are with sparse representations in some known space (or domain), information can be perfectly preserved even with small amount of measurements captured by random projections. Besides sparsity prior of signals, the i...