LA PLÉTHYSMOGRAPHIE RESPIRATOIRE PAR INDUCTANCE SANS ÉTALONNAGE. DÉVELOPPEMENTS EN EXPLORATION, SURVEILLANCE ET ASSISTANCE RESPIRATOIRES

Respiratory inductance plethysmography was used to measure thoracic and abdominal surfacearea changes in order to 1) detect inspiratory flow limitation induced using a devicedeveloped in our laboratory in healthy awake subjects; the detection method was based on theanalysis of the shape of the abdominal signal, 2) evaluate respiratory volume variations inorder to estimate air leaks during sleep in ventilated patients with neuromuscular disease;variations in the amplitude of thoracic and abdominal signals were used for this evaluation,and 3) detect swallows following water ingestion in elderly subjects; this detection used theflow signal obtained by differentiating the volume signal calculated from thoracic andabdominal signals. None of the three analyses required calibration of the respiratoryinductance plethysmography system.Les signaux des variations de sections thoracique (Tho) et abdominale (Abd) obtenus parpléthysmographie respiratoire par inductance ont été analysés pour 1) détecter des limitationsinspiratoires de débit chez le sujet sain induites à l'aide d'un dispositif mis au point aulaboratoire; cette méthode de détection est basée sur l'analyse de la forme du signal Abd, 2)évaluer les variations de volume pulmonaire afin d'en déduire l'évolution des fuites au coursde la nuit, chez des patients ayant des maladies neuromusculaire sous ventilation assistée;pour faire cette évaluation, les variations d'amplitudes des signaux Tho et Abd ont étéutilisées et 3) détecter les déglutitions provoquées par ingestion d'eau chez des personnesâgées; cette détection utilise le signal débit, obtenu par dérivation du signal volume calculé àpartir des signaux Abd et Tho. Aucune de ces trois analyses n'a nécessité l'étalonnage dusystème de pléthysmographie respiratoire par inductance

La pléthysmographie respiratoire par inductance sans étalonnage (développements en exploration, surveillance et assistance respiratoires)

Les signaux des variations de sections thoracique (Tho) et abdominale (Abd) obtenus par pléthysmographie respiratoire par inductance ont été analysés pour 1) détecter des limitations inspiratoires de débit chez le sujet sain induites à l'aide d'un dispositif mis au point au laboratoire; cette méthode de détection est basée sur l'analyse de la forme du signal Abd, 2) évaluer les variations de volume pulmonaire afin d'en déduire l'évolution des fuites au cours de la nuit, chez des patients ayant des maladies neuromusculaire sous ventilation assistée ; pour faire cette évaluation, les variations d'amplitudes des signaux Tho et Abd ont été utilisées et 3) détecter les déglutitions provoquées par ingestion d'eau chez des personnes âgées ; cette détection utilise le signal débit, obtenu par dérivation du signal volume calculé à partir des signaux Abd et Tho. Aucune de ces trois analyses n'a nécessité l'étalonnage du système de pléthysmographie respiratoire par inductance.GRENOBLE1-BU Sciences (384212103) / SudocSudocFranceF

Estimation of AHI using a single-channel EEG and home polygraphy signals

International audienc

Diagnosis of Sleep Apnea Without Sensors on the Patient's Face

International audienc

A Deep Survival Learning Approach for Cardiovascular Risk Estimation in Patients With Sleep Apnea

Cardiovascular (CV) disorders and obstructive sleep apnea (OSA) are very prevalent diseases worldwide. Multiple studies have demonstrated that OSA is associated with increased CV risk. Clinicians need to assess the CV risk to select the proper OSA treatment. A growing number of research have employed machine learning to predict CV risk by integrating clinical and sleep features. In this paper, a multiple input deep learning model was proposed to directly use sleep signals combined with clinical features. Data from 5,506 patients from the Pays de la Loire Sleep Cohort, without a history of major adverse cardiovascular events (MACE), investigated for OSA, were used. After a median follow-up of 6.0 years, 613 patients had been diagnosed with MACE according to the French national health system. Following an architecture selection, deep survival convolutional neural networks were computed to assess the MACE risk score. A custom loss function was integrated to consider the follow-up time of each patient. Based on the weights of each model input, a method for interpreting the model was also proposed to show the contribution of signals compared to clinical features. Sleep signals were extracted from a home sleep apnea test. The best results were obtained with the autonomic manifestation signal. An area under the ROC curve of 0.823 was reached. After interpretation of the models, consideration of sleep appeared to be more important in women and in those under 60. This method may help improve OSA patient care by estimating their risk of MACE during sleep diagnosis

Automatic identification of sleep and wakefulness using single-channel EEG and respiratory polygraphy signals for the diagnosis of obstructive sleep apnea

International audienc

La variation du rythme cardiaque post apnées-hypopnées, risque cardiovasculaire et réponse à la pression positive continue

National audienc

Intentional leaks in industrial masks have a significant impact on efficacy of bilevel noninvasive ventilation: a bench test study

BACKGROUND: During noninvasive ventilation, nonintentional leaks have a detrimental effect on the efficacy of ventilation. A wide range of industrial masks are available, with intentional leaks of different importance. The potential impact of this variability in intentional leaks on performances of bilevel ventilators has not been assessed. OBJECTIVE: To measure intentional leaks in seven different industrial masks and determine whether higher leaks modify ventilator performance and quality of ventilation. METHODS: Seven interfaces connected to four ventilators, the VPAP III ST (ResMed; NorthRyde, Australia), the BiPAP Harmony (Respironics; Monroeville, PA), the SmartAir ST (Covidien/Airox; Pau, France), and the GoodKnight 425 ST Bilevel (Covidien/Tyco-Nellcor/Puritan Bennett; Pleasanton, CA), were adapted on a mannequin connected to a lung model (ASL5000, IngMar Medical; Pittsburgh, PA). Inspiratory positive airway pressure (IPAP) and expiratory positive airway pressure were 14 and 4 cm H(2)O, respectively. The lung model was set with a respiratory rate of 15 cycles per min and a duration of inspiration of 1 s in three simulated conditions (normal, restrictive, and obstructive). Inspiratory trigger delay and effort, capacity to achieve and maintain IPAP, expiratory cycling and tidal volume were analyzed for all masks and ventilators in the three simulated lung conditions. RESULTS: The level of intentional leaks in the seven masks ranged from 30 to 45 L/min for an IPAP of 14 cm H(2)O. Importance of leaks did not influence trigger performances. However, capacity to achieve and maintain IPAP was significantly decreased with all ventilators and in all simulated lung conditions when intentional leaks increased. This led to a maximum reduction in delivered tidal volume of 48 mL. Expiratory cycling was not affected by the level of intentional leaks except in obstructive lung conditions. CONCLUSION: Mask intentional leaks can impair efficacy of ventilation, especially when > 40 L/min

High prevalence of obstructive sleep apnea in pregnant women with class III obesity: a prospective cohort study

STUDY OBJECTIVES: To determine the prevalence of obstructive sleep apnea (OSA) in a cohort of women with class III obesity, and a comparator lean group, in the second and third trimesters of pregnancy. Secondary objectives were to compare characteristics of women with obesity with and without OSA and to assess factors that were predictive of OSA. METHODS: We performed a prospective cohort study involving 33 women with class III obesity (mean body mass index 43.5 ± 3.9 kg/m(2)) and 39 lean women (body mass index 22.0 ± 1.7 kg/m(2)) with singleton pregnancies. Participants completed 2 level 3 sleep studies between 12–22 weeks and 32–38 weeks gestation. OSA was defined as a respiratory event index ≥ 5 events/h (≥ 3% desaturation criteria). Levels of interleukin-6, glucose, and C-peptide were quantified in maternal blood. Logistic regression analysis was performed to determine predictors of OSA. RESULTS: OSA was identified in 12 (37.5%) and 14 (50.0%) women with obesity and in 1 (2.6%) and 3 (9.1%) lean women in the second and third trimesters, respectively. Women with obesity with OSA were older than those with no OSA but otherwise had similar characteristics. In unadjusted analysis of women with obesity, increased age, body mass index, homeostatic model assessment of insulin resistance, and history of nonsmoking were associated with increased odds of OSA. In multivariable analysis, only increased age remained significantly associated with OSA. CONCLUSIONS: OSA is highly prevalent in pregnant women with class III obesity. Further research is required to establish effective management strategies for the growing number of women in this high-risk group. CITATION: Johns EC, Hill EA, Williams S, et al. High prevalence of obstructive sleep apnea in pregnant women with class III obesity: a prospective cohort study. J Clin Sleep Med. 2022;18(2):423–432

Hypoxic burden and heart-rate variability predict stroke incidence in sleep apnoea

International audienceManuscript word count: 1,374 "take home" message: Indices of sleep apnoea-related hypoxic burden and heart rate variability derived from full-night polysomnography might be useful for identifying sleep apnoea patients at risk for stroke