Multiresolution Minimization of Renyi's Mutual Information for fetal-ECG Extraction

Authors:
Fabio La Foresta;Nadia Mammone;Giuseppina Inuso;Francesco C. Morabito;Andrea Azzerboni
Affiliations:
DIMET-“Mediterranea” University of Reggio Calabria, Italy;DIMET-“Mediterranea” University of Reggio Calabria, Italy;DIMET-“Mediterranea” University of Reggio Calabria, Italy;DIMET-“Mediterranea” University of Reggio Calabria, Italy;Department of Gynecological, Obstetrics Sciences, and Reproductive Medicine, University of Messina, Italy
Venue:
Proceedings of the 2009 conference on New Directions in Neural Networks: 18th Italian Workshop on Neural Networks: WIRN 2008
Year:
2009

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Abstract

Fetal electrocardiogram (fECG) monitoring yields important information about the fetus condition during pregnancy and it consists in collecting electrical signals by some sensors on the body of the mother. In literature, Independent Component Analysis (ICA) has been exploited to extract fECG. Wavelet-ICA (WICA), a technique that merges Wavelet decomposition and INFOMAX algorithm for Independent Component Analysis, was recently proposed to enhance fetal ECG extraction. In this paper, we propose to enhance WICA introducing MERMAID as the algorithm to perform independent component analysis because it has shown to outperform INFOMAX and the other standard ICA algorithms.