A Hypothesis Testing Approach to Nonstationary Source Separation
This work addresses a specific bottleneck in signal processing for biomedical applications, offering an incremental improvement.
The paper tackles the problem of selecting appropriate matrices/tensors for joint diagonalization in nonstationary source separation by proposing a new framework based on hypothesis testing, which is applied to noninvasive fetal ECG extraction as a case study.
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 framework 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.