Information theoretic learning for inverse problem resolution in bio-electromagnetism

Nadia Mammone, Maurizio Fiasché, Giuseppina Inuso, Fabio La Foresta, Francesco Carlo Morabito, Mario Versaci

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper addresses the issue of learning directly from the observed data in Blind Source Separation (BSS), a particular inverse problem. This problem is very likely to occur when we are dealing with two or more independent electromagnetic sources. A powerful approach to BSS is Independent Component Analysis (ICA). This approach is much more powerful if no apriori assumption about data distribution is made: this is possible transferring as much information as possible to the learning machine defining a cost function based on an information theoretic criterion. In particular, Renyi's definition of entropy and mutual information are introduced and MERMAID (Minimum Renyi's Mutual Information), an algorithm for ICA based on such these definitions, is here described, implemented and tested over a popular BSS problem in bio-electromagnetism: fetal Electrocardiogram (fECG) extraction. MERMAID was compared to the well known algorithm INFOMAX and it showed to better learn from data and to provide a better source separation. The extracted fECG signals were finally postprocessed by wavelet analysis.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages414-421
Number of pages8
Volume4694 LNAI
EditionPART 3
Publication statusPublished - 2007
Event11th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2007, and 17th Italian Workshop on Neural Networks, WIRN 2007 - Vietri sul Mare, Italy
Duration: Sep 12 2007Sep 14 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume4694 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other11th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2007, and 17th Italian Workshop on Neural Networks, WIRN 2007
CountryItaly
CityVietri sul Mare
Period9/12/079/14/07

Keywords

  • Fetal ECG
  • Independent component analysis
  • Information theoretic learning
  • Wavelet transform

ASJC Scopus subject areas

  • Computer Science(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Theoretical Computer Science

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  • Cite this

    Mammone, N., Fiasché, M., Inuso, G., La Foresta, F., Morabito, F. C., & Versaci, M. (2007). Information theoretic learning for inverse problem resolution in bio-electromagnetism. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (PART 3 ed., Vol. 4694 LNAI, pp. 414-421). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4694 LNAI, No. PART 3).