Passive Nocturnal Physiologic Monitoring Enables Early Detection of Exacerbations in Children with Asthma. A Proof-of-Concept Study

Huffaker, Michelle F.; Carchia, Michael; Harris, Bronwyn; Kethman, William; Murphy, Todd E.; Sakarovitch, C; Qin, Feifei; Cornfield, David N.

doi:10.1164/rccm.201712-2606oc

Cited by 47 publications

(54 citation statements)

References 34 publications

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“…However, subjects also had infrequent episodes (median of three, over up to 2 years) when their RHR increased by more than two standard deviations above their average RHR. While we have no clinical data and thus cannot associate these episodes with any changes in health, others have found that increases in RHR preceded a formal diagnosis of acute infection and asthma exacerbations [30,31]. From these early studies, it is worth considering that a rising RHR may serve as an early warning sign of a physiologic change.…”

Section: Discussionmentioning

confidence: 87%

Inter- and intraindividual variability in daily resting heart rate and its associations with age, sex, sleep, BMI, and time of year: Retrospective, longitudinal cohort study of 92,457 adults

et al. 2020

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Background Heart rate is routinely measured as part of the clinical examination but is rarely acted upon unless it is well outside a population-based normal range. With wearable sensor technologies, heart rate can now be continuously measured, making it possible to accurately identify an individual's "normal" heart rate and potentially important variations in it over time. Our objective is to describe inter-and intra-individual variability in resting heart rate (RHR) collected over the course of two years using a wearable device, studying the variations of resting heart rate as a function of time of year, as well as individuals characteristics like age, sex, average sleep duration, and body mass index (BMI).

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Section: Discussionmentioning

confidence: 87%

Inter- and intraindividual variability in daily resting heart rate and its associations with age, sex, sleep, BMI, and time of year: Retrospective, longitudinal cohort study of 92,457 adults

et al. 2020

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“…There have been a variety of bed, chair, and other sensors proposed to capture the BCG; several are now available commercially [4][5][6][7][8][9][10][11][12][13][14][15][16][17]. Much of this work has focused on monitoring heart rate along with respiration rate from the accompanying respiration signal, and other parameters for tracking sleep quality.…”

Section: Introductionmentioning

confidence: 99%

Cardiovascular Function and Ballistocardiogram: A Relationship Interpreted via Mathematical Modeling

Guidoboni

Sala

Enayati

et al. 2019

IEEE Trans. Biomed. Eng.

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Objective: to develop quantitative methods for the clinical interpretation of the ballistocardiogram (BCG). Methods: a closed-loop mathematical model of the cardiovascular system is proposed to theoretically simulate the mechanisms generating the BCG signal, which is then compared with the signal acquired via accelerometry on a suspended bed. Results: simulated arterial pressure waveforms and ventricular functions are in good qualitative and quantitative agreement with those reported in the clinical literature. Simulated BCG signals exhibit the typical I, J, K, L, M and N peaks and show good qualitative and quantitative agreement with experimental measurements. Simulated BCG signals associated with reduced contractility and increased stiffness of the left ventricle exhibit different changes that are characteristic of the specific pathological condition. Conclusion: the proposed closed-loop model captures the predominant features of BCG signals and can predict pathological changes on the basis of fundamental mechanisms in cardiovascular physiology. Significance: this work provides a quantitative framework for the clinical interpretation of BCG signals and the optimization of BCG sensing devices. The present study considers an average human body and can potentially be extended to include variability among individuals.

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“…Researchers in (Huffaker et al, 2018) said that detecting the asthma at an early stage is a big challenge. They have used the random forest machine learning model to predict the asthma exacerbation in the children.…”

Section: Resultsmentioning

confidence: 99%

Use of AI and Machine Learning for Asthma Patients: A Systematic Literature Review

Fadziso

Choi²

2020

Malays. j. med. biol. res.

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In modern times, the collection of data is not a big deal but using it in a meaningful is a challenging task. Different organizations are using artificial intelligence and machine learning for collecting and utilizing the data. These should also be used in the medical because different disease requires the prediction. One of these diseases is asthma that is continuously increasing and affecting more and more people. The major issue is that it is difficult to diagnose in children. Machine learning algorithms can help in diagnosing it early so that the doctors can start the treatment early. Machine learning algorithms can perform this prediction so this study will be helpful for both the doctors and patients. There are different machine learning predictive algorithms are available that have been used for this purpose.

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Passive Nocturnal Physiologic Monitoring Enables Early Detection of Exacerbations in Children with Asthma. A Proof-of-Concept Study

Abstract: Nocturnal physiologic changes correlate with asthma symptoms, supporting the notion that nocturnal physiologic monitoring represents an objective diagnostic tool capable of longitudinally assessing disease control and predicting asthma exacerbations in children with asthma at home.

Cited by 47 publications

References 34 publications

Inter- and intraindividual variability in daily resting heart rate and its associations with age, sex, sleep, BMI, and time of year: Retrospective, longitudinal cohort study of 92,457 adults

Inter- and intraindividual variability in daily resting heart rate and its associations with age, sex, sleep, BMI, and time of year: Retrospective, longitudinal cohort study of 92,457 adults

Cardiovascular Function and Ballistocardiogram: A Relationship Interpreted via Mathematical Modeling

Use of AI and Machine Learning for Asthma Patients: A Systematic Literature Review

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