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Communication Dans Un Congrès Année : 2022

Drowsiness detection from polysomnographic data using multivariate selfsimilarity and eigen-wavelet analysis

Résumé

Because drowsiness is a major cause in vehicle accidents, its automated detection is critical. Scale-free temporal dynamics is known to be typical of physiological and body rhythms. The present work quantifies the benefits of applying a recent and original multivariate selfsimilarity analysis to several modalities of polysomnographic measurements (heart rate, blood pressure, electroencephalogram and respiration), from the MIT-BIH Polysomnographic Database, to better classify drowsiness-related sleep stages. Clinical relevance-This study shows that probing jointly temporal dynamics amongst polysomnographic measurements, with a proposed original multivariate multiscale approach, yields a gain of above 5% in the Area-under-Curve quantifying drowsiness-related sleep stage classification performance compared to univariate analysis.
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Dates et versions

hal-03850439 , version 1 (21-07-2022)
hal-03850439 , version 2 (13-11-2022)

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Charles-Gérard Lucas, Patrice Abry, Herwig Wendt, Gustavo Didier. Drowsiness detection from polysomnographic data using multivariate selfsimilarity and eigen-wavelet analysis. 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC 2022), IEEE Engineering in Medicine & Biology Society, Jul 2022, Glasgow, United Kingdom. ⟨10.1109/EMBC48229.2022.9871363⟩. ⟨hal-03850439v2⟩
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