Key result
A general deflation framework successfully separated desired signal subspaces from noisy multichannel observations, including extracting fetal cardiac signals from maternal abdominal recordings.
A novel deflation framework for subspace decomposition can effectively extract fetal cardiac signals and diaphragmatic electromyograms from noisy multichannel observations.
May aid non-invasive fetal monitoring; leaves open prospective clinical validation of the deflation framework.
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.
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Sameni et al. (2009) studied Fetal cardiac signals and diaphragmatic electromyogram extraction. Deflation framework for subspace decomposition was evaluated on Separation of a desired signal subspace from noisy multichannel observations. A general deflation framework successfully separated desired signal subspaces from noisy multichannel observations, including extracting fetal cardiac signals from maternal abdominal recordings.
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