Probabilistic model definition for physiological state monitoring

Laure Amate 1 Florence Forbes 2 Julie Fontecave-Jallon 3 Benoît Vettier 1 Catherine Garbay 1, *
* Auteur correspondant
2 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
3 PRETA
TIMC-IMAG - Techniques de l'Ingénierie Médicale et de la Complexité - Informatique, Mathématiques et Applications [Grenoble]
Abstract : Assessing the global situation of a person from physiological data is a well-known difficult problem. In previous work, we propose a system that does not produce a diagnosis but instead follows a set of hypotheses and decides of an alarming situation with this information. In this paper we focus on data processing part of the system taking into account the complexity and the ambiguity of the data. We propose a statistical approach with a global model based on Hidden Markov Model and we present data models that rely on classical physiological parameters and expert's knowledge. We then learn a model that depends on the person and its environment, and we define and compute confidence values to assess the plausibility of hypotheses.
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Communication dans un congrès
SSP 2011 - Statistical Signal Processing Workshop, Jun 2011, Nice, France. pp.457-460, 2011, <10.1109/SSP.2011.5967730>
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Soumis le : mardi 16 octobre 2012 - 09:56:14
Dernière modification le : mercredi 29 juillet 2015 - 01:22:30
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Laure Amate, Florence Forbes, Julie Fontecave-Jallon, Benoît Vettier, Catherine Garbay. Probabilistic model definition for physiological state monitoring. SSP 2011 - Statistical Signal Processing Workshop, Jun 2011, Nice, France. pp.457-460, 2011, <10.1109/SSP.2011.5967730>. <hal-00742161>

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