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Automatic Sleep Stages Classification Combining Semantic Representation and Dynamic Expert System

Abstract : Interest in sleep has been growing in the last decades, considering its benefits for well-being, but also to diagnose sleep troubles. The gold standard to monitor sleep consists of recording the course of many physiological parameters during a whole night. The human interpretation of resulting curves is time consuming. We propose an automatic knowledge-based decision system to support sleep staging. This system handles temporal data, such as events, to combine and aggregate atomic data, so as to obtain high-abstraction-levels contextual decisions. The proposed system relies on a semantic reprentation of observations, and on contextual knowledge base obtained by formalizing clinical practice guidelines. Evaluated on a dataset composed of 131 full night polysomnographies, results are encouraging, but point out that further knowledge need to be integrated.
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https://hal.archives-ouvertes.fr/hal-02314668
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Submitted on : Sunday, October 13, 2019 - 12:13:20 PM
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Adrien Ugon, Carole Philippe, Amina Kotti, Marie-Amélie Dalloz, Andrea Pinna. Automatic Sleep Stages Classification Combining Semantic Representation and Dynamic Expert System. MEDINFO 2019: Health and Wellbeing e-Networks for All, Aug 2019, Lyon, France. pp.848-852, ⟨10.3233/SHTI190343⟩. ⟨hal-02314668⟩

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