Thèse soutenue

Fusion symbolique et données polysomnographiques
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Auteur / Autrice : Adrien Ugon
Direction : Jean-Gabriel Ganascia
Type : Thèse de doctorat
Discipline(s) : Informatique
Date : Soutenance en 2013
Etablissement(s) : Paris 6

Mots clés

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Résumé

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In recent decades, medical examinations required to diagnose and guide to treatmentbecame more and more complex. It is even a current practice to use several examinationsin different medical specialties to study a disease through multiple approaches so as todescribe it more deeply. The interpretation is difficult because the data is both heterogeneous and also veryspecific, with skilled domain of knowledge required to analyse it. In this context, symbolic fusion appears to be a possible solution. Indeed, it wasproved to be very effective in treating problems with low or high levels of abstraction ofinformation to develop a high level knowledge. This thesis demonstrates the effectiveness of symbolic fusion applied to the treatmentof polysomnographic data for the development of an assisted diagnosis tool of Sleep ApneaSyndrome. Proper diagnosis of this sleep disorder requires a polysomnography. This medicalexamination consists of simultaneously recording of various physiological parametersduring a night. Visual interpretation is tedious and time consuming and there commonlyis some disagreement between scorers. The use of a reliable support-to-diagnosis toolincreases the consensus. This thesis develops stages of the development of such a tool.