Why the study?
While numerous algorithms address nonstationary signal extraction, selecting the diagonalized matrix or tensor set remains highly problem-specific.
Proposes a novel hypothesis testing framework for semi-blind source separation of nonstationary signals, with potential applications in fetal ECG extraction.
May aid noninvasive fetal ECG extraction; leaves open prospective validation before clinical use.
The extraction of nonstationary signals from blind and semi-blind multivariate observations is a recurrent problem. Numerous algorithms have been developed for this problem, which are based on the exact or approximate joint diagonalization of second or higher order cumulant matrices/tensors of multichannel data. While a great body of research has been dedicated to joint diagonalization algorithms, the selection of the diagonalized matrix/tensor set remains highly problem-specific. Herein, various methods for nonstationarity identification are reviewed and a new general frame-work based on hypothesis testing is proposed, which results in a classification/clustering perspective to semi-blind source separation of nonstationary components. The proposed method is applied to noninvasive fetal ECG extraction, as case study.
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Sameni et al. (2021) studied this question.
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