Could home-based FeNO measurements breathe new life into asthma management?

Rapson, Trevor D.; Hall, Graham L.; Sutherland, Tara D.

doi:10.1080/02770903.2018.1493604

Cited by 2 publications

(2 citation statements)

References 28 publications

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“…Nevertheless, some people with asthma do not perceive their symptoms sufficiently well [ 14 , 15 ] and, thus, do not realize that their condition is deteriorating. By monitoring FeNO, patients can compare their daily symptoms with the monitoring results, and, subsequently, this can help them to manage and avoid the worsening of their asthma in a timely manner [ 16 ]. According to the type of conversion signal, common NO sensors can be categorized as either electrochemical or optical sensors [ 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

Multivariate Regression in Conjunction with GA-BP for Optimization of Data Processing of Trace NO Gas Flow in Active Pumping Electronic Nose

Sun

Shi

2023

Sensors

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Exhaled nitric oxide trace gas at the ppb level is a biomarker of human airway inflammation. To detect this, we developed a method for the collection of active pumping electronic nose bionic chamber gas. An optimization algorithm based on multivariate regression (MR) and genetic algorithm–back propagation (GA-BP) was proposed to improve the accuracy of trace-level gas detection. An electronic nose was used to detect NO gas at the ppb level by substituting breathing gas with a sample gas. The impact of the pump suction flow capacity variation on the response of the electronic nose system was determined using an ANOVA. Further, the optimization algorithm based on MR and GA-BP was studied for flow correction. The results of this study demonstrate an increase in the detection accuracy of the system by more than twofold, from 17.40%FS before correction to 6.86%FS after correction. The findings of this research lay the technical groundwork for the practical application of electronic nose systems in the daily monitoring of FeNO.

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Section: Introductionmentioning

confidence: 99%

Multivariate Regression in Conjunction with GA-BP for Optimization of Data Processing of Trace NO Gas Flow in Active Pumping Electronic Nose

Sun

Shi

2023

Sensors

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“…At present, quantitative testing for asthma markers is only available in hospitals under the supervision of professional medical staff [ 7 ]. However, some asthma patients are not fully aware of their own symptoms and their worsening condition [ 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

Bionic sensing system and characterization of exhaled nitric oxide detection based on canine olfaction

Sun

Shi

2022

PLoS ONE

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A quantitative monitoring system for fractional exhaled nitric oxide (FENO) in homes is very important for the control of respiratory diseases such as asthma. To this end, this paper proposes a small bionic sensing system for NO detection in an electronic nose based on analysis of the structure of the canine olfactory system and the airflow pattern in the nasal cavity. The proposed system detected NO at different FENO concentration levels with different bionic sensing systems in the electronic nose, and analyzed the data comparatively. Combined with a backpropagation neural network algorithm, the bionic canine sensing system improved the recognition rate for FENO detection by up to 98.1%. Moreover, electronic noses with a canine bionic sensing system can improve the performance of trace gas detection.

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Could home-based FeNO measurements breathe new life into asthma management?

Cited by 2 publications

References 28 publications

Multivariate Regression in Conjunction with GA-BP for Optimization of Data Processing of Trace NO Gas Flow in Active Pumping Electronic Nose

Multivariate Regression in Conjunction with GA-BP for Optimization of Data Processing of Trace NO Gas Flow in Active Pumping Electronic Nose

Bionic sensing system and characterization of exhaled nitric oxide detection based on canine olfaction

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