Fetal electrocardiogram extraction by sequential source separation in the wavelet domain.
This paper addresses the problem of fetal electrocardiogram extraction using blind source separation (BSS) in the wavelet domain. A new approach is proposed, which is particularly advantageous when the mixing environment is noisy and time-varying, and that is shown, analytically and in simulation, to improve the convergence rate of the natural gradient algorithm. The distribution of the wavelet coefficients of the source signals is then modeled by a generalized Gaussian probability density, whereby in the time-scale domain the problem of selecting appropriate nonlinearities when separating mixtures of both sub- and super-Gaussian signals is mitigated, as shown by experimental results.
Jafari
Maria G
MG
Centre for Digital Music, Department of Electronic Engineering, Queen Mary University of London, London, E1 4NS, UK. maria.jafari@elec.qmul.ac.uk
Chambers
Jonathon A
JA
D000465:Algorithms; D018780:Body Surface Potential Mapping / Q000379:methods*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004562:Electrocardiography / Q000379:methods*; D005260:Female; D005323:Fetal Monitoring / Q000379:methods*; D006801:Humans; D008955:Models, Cardiovascular; D008959:Models, Neurological; D015233:Models, Statistical; D011247:Pregnancy; D012815:Signal Processing, Computer-Assisted; D013269:Stochastic Processes