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Communication Dans Un Congrès Année : 2021

Automated Quiet Sleep Detection for Premature Newborns Based on Video and ECG Analysis

Résumé

A newborn is preterm if birth occurred before a gestational age of 37 weeks. He has several immature functions, which implies a specific monitoring and, among others, the analysis of its sleep. Here we make a focus on Quiet Sleep (QS), whose increasing is primordial with age, and characterized by an absence of motion and a regular cardiorespiratory rhythm. A method to automatically detect QS is proposed, on the basis of a video analysis (detection of motion), supplemented by the estimation of ECG and respiration 'qualities'. This approach combines feature extraction and machine learning methods. It was validated on a set of 15 newborns and 25 eight-hours recordings manually annotated. Best results were obtained by combining non-motion intervals and ECG quality, but showing also an overestimation of QS (Se=88%, Sp=49%). However, regarding extracted features, we observed similar trends between manual and automated QS, with an increasing of average duration of QS intervals and percentage of time in QS with age, also approaching values of the full-term newborns. Finally, computation of QS on a larger set of 45 recordings confirmed the interest of the approach for maturation evaluation purposes. © 2021 Creative Commons.
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Dates et versions

hal-03596093 , version 1 (03-03-2022)

Identifiants

Citer

S. Cabon, R. Weber, L. Cailleau, Guy Carrault, P. Pladys, et al.. Automated Quiet Sleep Detection for Premature Newborns Based on Video and ECG Analysis. 2021 Computing in Cardiology, CinC 2021, Sep 2021, Brno, Czech Republic. ⟨10.23919/CinC53138.2021.9662821⟩. ⟨hal-03596093⟩
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