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”The Extraction of Fetal ECG using Multi-Modality”


Co-encadrant :     Bertrand RIVET

École doctorale : Electronique, electrotechnique, automatique, traitement du signal (EEATS)

Spécialité : Signal, image, parole, télécoms

Structure de rattachement : UJF

Établissement d'origine : UJF

Financement(s) : Contrat doctoral


Date d'entrée en thèse : 01/10/2012

Date de soutenance : 02/11/2015


Composition du jury :
M. Christian Jutten, Professeur, Université Joseph Fourier
M. Mohammad Bagher Shamsollahi, Professeur, Université de technologie de Sharif
M. Philippe Ravier, Maître de conférences, Université d'Orléans
M. Reza Sameni, Maître de conférences, Université de Shiraz
M. Pierre-Yves Guméry, Professeur, Université Joseph Fourier
M. Bertrand Rivet, Maître de conférences, Grenoble INP
Mme. Véronique Equy, Gynécologue-Obstétricien, CHU de Grenoble


Résumé : Technological development aims at monitoring during pregnancy using the noninvasive fetal electrocardiogram (ECG). This method allows not only to detect fetal heart rate, but also to analyze the morphology of fetal ECG, which is now limited to analysis of the invasive ECG during delivery. However, the noninvasive fetal ECG recorded from the mother's abdomen is contaminated with several noise sources among which the maternal ECG is the most prominent. That is why this problem is still a challenge in the research which is handled by uni-modal approaches, up to now. In the present study, the problem of noninvasive fetal ECG extraction is tackled using multi-modality. In the multi-modal concept, beside ECG signal, this approach benefits from the phonocardiogram (PCG) signal as another signal modality, which can provide complementary information about the fetal ECG. A general method for quasi-periodic signal analysis and modeling is described, and its application to ECG denoising and fetal ECG extraction is explained. Multi-modality is based on the Gaussian process modeling, in this study, in order to provide the possibility of flexible models and nonlinear estimations.

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