Elements of information theory
Elements of information theory
Independent component analysis for identification of artifacts in magnetoencephalographic recordings
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Independent components of magnetoencephalography: localization
Neural Computation
An analysis of entropy estimators for blind source separation
Signal Processing
Partitioned factor analysis for interference suppression and source extraction
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Journal of Computational and Applied Mathematics
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Fetal magnetocardiographic sensor measurements are contaminated by undesired environmental and biological signals, such as the maternal cardiac signal. Several methods have been used in an attempt to extract the fetal cardiac signal from these data, which are based on, e.g., the presumed quasi-periodicity of the maternal cardiac signal or the presumed statistical independence between the fetal cardiac signal and interfering signals. Recently a different type of method for extracting signals from noisy data has been introduced. This probabilistic method, known as partitioned factor analysis (PFA), assumes that the data can be partitioned into periods of source inactivity and source activity. PFA was originally developed for stimulus-evoked, trial-averaged encephalographic data, for which the partitions are known in advance. Here we show how to use PFA for extracting the fetal cardiac signal from cardiographic data, for which the partitions must be determined from the data. In addition, we show that PFA can be used even when the partitions cannot be determined directly from the data.