Signals & systems (2nd ed.)
Matrix computations (3rd ed.)
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Multichannel ECG and noise modeling: application to maternal and fetal ECG signals
EURASIP Journal on Applied Signal Processing
Iterative Subspace Decomposition for Ocular Artifact Removal from EEG Recordings
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
A blind source separation technique using second-order statistics
IEEE Transactions on Signal Processing
Blind separation of speech mixtures via time-frequency masking
IEEE Transactions on Signal Processing
Underdetermined blind source separation based on sparse representation
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
De-noising by soft-thresholding
IEEE Transactions on Information Theory
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
Computer Methods and Programs in Biomedicine
Hi-index | 35.68 |
A general deflation framework is described for the separation of a desired signal subspace of arbitrary dimensions from noisy multichannel observations. The method simultaneously uses single and multichannel priors to split the desired and undesired subspaces, even for coplanar (intersecting) subspaces. By appropriate use of signal priors, it can even extract signals from degenerate mixtures of signals and noise recorded from a few number of channels in low SNR scenarios, without the reduction of the data dimensions. As a case study, the performance of the proposed method is studied for the problem of extracting fetal cardiac signals from maternal abdominal recordings, over simulated and real data. A second case study deals with the degenerate problem of extracting diaphragmatic electromyogram from electrocardiograph artifacts. A provisional patent application based on this method has been filed.