A Physical Model of Inspiratory Flow Limitation in Awake Healthy Subjects

Sabil, AbdelKébir; Eberhard, André; Baconnier, Pierre; Benchetrit, Gila

doi:10.1007/0-387-27023-x_32

Cited by 9 publications

(9 citation statements)

References 13 publications

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“…Apart from the commonly appreciated plateau in inspiratory flow, flow limitation is present when peak flow occurs very early in inspiration and then declines or it occurs very late in inspiration. Flow limitation was expressed as % of breaths with flow limitation 21. Measurements were made over the entire night and in NREM and REM sleep.…”

Section: Methodsmentioning

confidence: 99%

Association of novel measures of sleep disturbances with blood pressure: the Multi-Ethnic Study of Atherosclerosis

Kim

Azarbarzin

Wang

et al. 2019

Thorax

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BackgroundMechanisms underlying blood pressure (BP) changes in obstructive sleep apnoea (OSA) are incompletely understood. We assessed the associations between BP and selected polysomnography (PSG) traits: sleep depth, airflow limitation measurements and OSA-specific hypoxic burden.MethodsThis cross-sectional analysis included 2055 participants from the Multi-Ethnic Study of Atherosclerosis who underwent PSG and BP measurements in 2010–2013. Sleep depth was assessed using the ‘OR product’, a continuous measure of arousability. Airflow limitation was assessed by duty cycle (Ti/Tt) and % of breaths with flow limitation, and hypoxia by ‘hypoxic burden’. Primary outcomes were medication-adjusted systolic BP (SBP) and diastolic BP (DBP). We used generalised linear models adjusted for age, sex, race/ethnicity, smoking, education, body mass index, alcohol use, periodic limb movements and alternative physiological disturbances.ResultsThe sample had a mean age of 68.4 years and apnoea–hypopnoea index of 14.8 events/hour. Sleep depth was not significantly associated with BP. Every 1 SD increment in log-transformed non-rapid eye movement duty cycle was associated with 0.9% decrease in SBP (95% CI: 0.1% to 1.6%), even after adjusting for sleep depth and hypoxic burden. Every 1 SD increment in log-transformed hypoxic burden was associated with a 1.1% increase in SBP (95% CI: 0.1% to 2.1%) and 1.9% increase in DBP (95% CI: 1.0% to 2.8%) among those not using hypertension medications.ConclusionsHigher duty cycle was associated with lower SBP overall and hypoxic burden with higher SBP and DBP among non-BP medication users. These findings suggest changes in both respiratory effort and oxygenation during sleep influence BP.

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Section: Methodsmentioning

confidence: 99%

Association of novel measures of sleep disturbances with blood pressure: the Multi-Ethnic Study of Atherosclerosis

Kim

Azarbarzin

Wang

et al. 2019

Thorax

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“…These findings and those of other studies 48 , 49 suggest that the respiratory inductive plethysmography system, integrated into the jacket, provides reasonable estimates of respiratory cycle time, V T , RR, and

{\overset{V}{true}}_{normalE}

during resting conditions and exercise in different types of populations. However, the main application of these instrumented garments (Visuresp and LifeShirt) is the potential for monitoring of vital signs outside a hospital 48 – 52 . Furthermore, no application has been developed to attempt to estimate energy expenditure from the variables measured and/or estimated by these new clothes.…”

Section: Methods Of Measurement and Estimation Of Ventilation: Applicmentioning

confidence: 99%

“…However, the main application of these instrumented garments (Visuresp and LifeShirt) is the potential for monitoring of vital signs outside a hospital. [48][49][50][51][52] Furthermore, no application has been developed to attempt to estimate energy expenditure from the variables measured and/or estimated by these new clothes. These instrumented garments represent the future in physical activity monitoring.…”

Section: The Lifeshirtmentioning

confidence: 99%

A Review of the Evidence for the Use of Ventilation as a Surrogate Measure of Energy Expenditure

Gastinger

Donnelly

Dumond³

et al. 2014

J Parenter Enteral Nutr

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Precise measurement of sedentary behavior and physical activity is necessary to characterize the dose-response relationship between these variables and health outcomes. The most frequently used methods employ portable devices to measure mechanical or physiological parameters (eg, pedometers, heart rate monitors, accelerometers). There is considerable variability in the accuracy of total energy expenditure (TEE) estimates from these devices. This review examines the potential of measurement of ventilation (VE) to provide an estimate of free-living TEE. The existence of a linear relationship between VE and energy expenditure (EE) was demonstrated in the mid-20th century. However, few studies have investigated this parameter as an estimate of EE due to the cumbersome equipment required to measure VE. Portable systems that measure VE without the use of a mouthpiece have existed for about 20 years (respiratory inductive plethysmography). However, these devices are adapted for clinical monitoring and are too cumbersome to be used in conditions of daily life. Technological innovations of recent years (small electromagnetic coils glued on the chest/back) suggest that VE could be estimated from variations in rib cage and abdominal distances. This method of TEE estimation is based on the development of individual/group calibration curves to predict the relationship between ventilation and oxygen consumption. The new method provides a reasonably accurate estimate of TEE in different free-living conditions such as sitting, standing, and walking. Further work is required to integrate these electromagnetic coils into a jacket or T-shirt to create a wearable device suitable for long-term use in free-living conditions.

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“…In recent years, IFL events have been recognized as a vital diagnostic tool for mild SDB [8, 9]. According to the Stanford Sleep Clinic scoring rule [10], for diagnosing mild SDB, at least four successive breaths must be characterized by an abnormal wave contour.…”

Section: Introductionmentioning

confidence: 99%

Weighted Polynomial Approximation for Automated Detection of Inspiratory Flow Limitation

Huang

Jan

et al. 2017

Computational and Mathematical Methods in Medicine

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Inspiratory flow limitation (IFL) is a critical symptom of sleep breathing disorders. A characteristic flattened flow-time curve indicates the presence of highest resistance flow limitation. This study involved investigating a real-time algorithm for detecting IFL during sleep. Three categories of inspiratory flow shape were collected from previous studies for use as a development set. Of these, 16 cases were labeled as non-IFL and 78 as IFL which were further categorized into minor level (20 cases) and severe level (58 cases) of obstruction. In this study, algorithms using polynomial functions were proposed for extracting the features of IFL. Methods using first- to third-order polynomial approximations were applied to calculate the fitting curve to obtain the mean absolute error. The proposed algorithm is described by the weighted third-order (w.3rd-order) polynomial function. For validation, a total of 1,093 inspiratory breaths were acquired as a test set. The accuracy levels of the classifications produced by the presented feature detection methods were analyzed, and the performance levels were compared using a misclassification cobweb. According to the results, the algorithm using the w.3rd-order polynomial approximation achieved an accuracy of 94.14% for IFL classification. We concluded that this algorithm achieved effective automatic IFL detection during sleep.

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A Physical Model of Inspiratory Flow Limitation in Awake Healthy Subjects

Cited by 9 publications

References 13 publications

Association of novel measures of sleep disturbances with blood pressure: the Multi-Ethnic Study of Atherosclerosis

Association of novel measures of sleep disturbances with blood pressure: the Multi-Ethnic Study of Atherosclerosis

A Review of the Evidence for the Use of Ventilation as a Surrogate Measure of Energy Expenditure

Weighted Polynomial Approximation for Automated Detection of Inspiratory Flow Limitation

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