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. 2021 Nov 15;204(10):1227-1231.
doi: 10.1164/rccm.202103-0680LE.

Machine Learning-based Sleep Staging in Patients with Sleep Apnea Using a Single Mandibular Movement Signal

Nhat-Nam Le-Dong  1 , Jean-Benoit Martinot  2   3 , Nathalie Coumans  2 , Valérie Cuthbert  2 , Renaud Tamisier  4   5 , Sébastien Bailly  4   5 , Jean-Louis Pépin  4   5
Affiliations

Machine Learning-based Sleep Staging in Patients with Sleep Apnea Using a Single Mandibular Movement Signal

Nhat-Nam Le-Dong et al. Am J Respir Crit Care Med. .
No abstract available

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Figures

Figure 1.
Figure 1.
The mandibular movements (MM) signal processed by machine learning to provide sleep staging. Typical example of two of the six channels (upper and lower trace) of the MM signal recorded by a single sensor during the four sleep stages in a single individual. Each trace represents a 210-second (3.5-min) time span of MM recordings by the Sunrise system (inertial measurement with six channels) during wakefulness (top), REM sleep, light sleep, and deep sleep (bottom). Thirty-second epochs were used for sleep stage classification. Sleep is detected when MM occur at the breathing frequency. During light sleep (N2), the amplitude of MM reaches several tenths of a millimeter and varies slightly. The movements during quiet respiration and light sleep are repeated at a frequency ranging between 0.15 and 0.60 Hz depending on central drive output. Deepening of sleep (N3) increases the upper airway’s resistance, and this is reflected by an increase in the amplitude of movement, which is also more stable than during N2. REM sleep is easily identified by irregular frequencies and changing amplitudes in MM that are on average smaller than non-REM sleep amplitudes. Cartoon images adapted from Freepik.com.
Figure 2.
Figure 2.
Stagewise receiver operating characteristics (ROC) curve analysis. This consisted of extracting prediction scores for each target stage (wake, light sleep, deep sleep, and REM sleep) and for each patient, then estimating the false and true positive rates of a binary one-versus-rest classification rule to establish the ROC curve. The 95% CIs of the area under the curve (AUC) and smoothing effect were obtained from empirical data (without using any resampling). The diagonal dashed line serves as a reference and shows the performance if sleep staging had been made randomly. The algorithm performed well in detecting REM sleep with a ROC–AUC of 0.96 (0.90–0.99) and non-REM deep sleep with a ROC–AUC of 0.97 (0.91–0.99). Only light non-REM sleep was slightly less well detected with an ROC–AUC of 0.86 (0.77–0.94). CI = confidence interval; DS = deep sleep; LS = light sleep; R = REM sleep; W = wake.
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References

    1. Benjafield AV, Ayas NT, Eastwood PR, Heinzer R, Ip MSM, Morrell MJ, et al. Estimation of the global prevalence and burden of obstructive sleep apnoea: a literature-based analysis. Lancet Respir Med . 2019;7:687–698. - PMC - PubMed
    1. Kubin L. Neural control of the upper airway: respiratory and state-dependent mechanisms. Compr Physiol. 2016;6:1801–1850. - PMC - PubMed
    1. Moore JD, Kleinfeld D, Wang F. How the brainstem controls orofacial behaviors comprised of rhythmic actions. Trends Neurosci . 2014;37:370–380. - PMC - PubMed
    1. Pépin JL, Letesson C, Le-Dong NN, Dedave A, Denison S, Cuthbert V, et al. Assessment of mandibular movement monitoring with machine learning analysis for the diagnosis of obstructive sleep apnea. JAMA Netw Open . 2020;3:e1919657. - PMC - PubMed
    1. Martinot JB, Borel JC, Cuthbert V, Guénard HJP, Denison S, Silkoff PE, et al. Mandibular position and movements: Suitability for diagnosis of sleep apnoea. Respirology . 2017;22:567–574. - PubMed

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