scholarly journals Electronic nose dataset for COPD detection from smokers and healthy people through exhaled breath analysis

Data in Brief ◽  
2021 ◽  
Vol 35 ◽  
pp. 106767
Author(s):  
Cristhian Manuel Durán Acevedo ◽  
Carlos A. Cuastumal Vasquez ◽  
Jeniffer Katerine Carrillo Gómez
Keyword(s):  
Electronic Nose ◽  
Breath Analysis ◽  
Healthy People ◽  
Exhaled Breath ◽  
Exhaled Breath Analysis

scholarly journals Sensor array optimization techniques for exhaled breath analysis to discriminate diabetics using an electronic nose

ETRI Journal ◽  
2018 ◽  
Vol 40 (6) ◽  
pp. 802-812 ◽  
Author(s):  
Jin-Young Jeon ◽  
Jang-Sik Choi ◽  
Joon-Boo Yu ◽  
Hae-Ryong Lee ◽  
Byoung Kuk Jang ◽  
...  
Keyword(s):  
Electronic Nose ◽  
Sensor Array ◽  
Breath Analysis ◽  
Optimization Techniques ◽  
Exhaled Breath ◽  
Exhaled Breath Analysis ◽  
Array Optimization

scholarly journals MOS Sensors Array for the Discrimination of Lung Cancer and At-Risk Subjects with Exhaled Breath Analysis

Chemosensors ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 209
Author(s):  
Davide Marzorati ◽  
Luca Mainardi ◽  
Giulia Sedda ◽  
Roberto Gasparri ◽  
Lorenzo Spaggiari ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Survival Rate ◽  
Mortality Rate ◽  
Early Diagnosis ◽  
Gas Sensor ◽  
Electronic Nose ◽  
Gas Sensors ◽  
Breath Analysis ◽  
Exhaled Breath ◽  
Exhaled Breath Analysis

Lung cancer is characterized by a tremendously high mortality rate and a low 5-year survival rate when diagnosed at a late stage. Early diagnosis of lung cancer drastically reduces its mortality rate and improves survival. Exhaled breath analysis could offer a tool to clinicians to improve the ability to detect lung cancer at an early stage, thus leading to a reduction in the associated survival rate. In this paper, we present an electronic nose for the automatic analysis of exhaled breath. A total of five a-specific gas sensors were embedded in the electronic nose, making it sensitive to different volatile organic compounds (VOCs) contained in exhaled breath. Nine features were extracted from each gas sensor response to exhaled breath, identifying the subject breathprint. We tested the electronic nose on a cohort of 80 subjects, equally split between lung cancer and at-risk control subjects. Including gas sensor features and clinical features in a classification model, recall, precision, and accuracy of 78%, 80%, and 77% were reached using a fourfold cross-validation approach. The addition of other a-specific gas sensors, or of sensors specific to certain compounds, could improve the classification accuracy, therefore allowing for the development of a clinical tool to be integrated in the clinical pipeline for exhaled breath analysis and lung cancer early diagnosis.

scholarly journals Ruling out SARS-CoV-2 infection using exhaled breath analysis by electronic nose in a public health setting

2021 ◽  
Author(s):  
Rianne de Vries ◽  
René M. Vigeveno ◽  
Simone Mulder ◽  
Niloufar Farzan ◽  
Demi R. Vintges ◽  
...  
Keyword(s):  
Public Health ◽  
Electronic Nose ◽  
Breath Analysis ◽  
Test Facility ◽  
Exhaled Breath ◽  
Non Invasive ◽  
Flight Mass Spectrometry ◽  
Exhaled Breath Analysis ◽  
Invasive Method ◽  
Rule Out

AbstractBackgroundRapid and accurate detection of SARS-CoV-2 infected individuals is crucial for taking timely measures and minimizing the risk of further SARS-CoV-2 spread. We aimed to assess the accuracy of exhaled breath analysis by electronic nose (eNose) for the discrimination between individuals with and without a SARS-CoV-2 infection.MethodsThis was a prospective real-world study of individuals presenting to public test facility for SARS-CoV-2 detection by molecular amplification tests (TMA or RT-PCR). After sampling of a combined throat/nasopharyngeal swab, breath profiles were obtained using a cloud-connected eNose. Data-analysis involved advanced signal processing and statistics based on independent t-tests followed by linear discriminant and ROC analysis. Data from the training set were tested in a validation, a replication and an asymptomatic set.FindingsFor the analysis 4510 individuals were available. In the training set (35 individuals with; 869 without SARS-CoV-2), the eNose sensors were combined into a composite biomarker with a ROC-AUC of 0.947 (CI:0.928-0.967). These results were confirmed in the validation set (0.957; CI:0.942-0.971, n=904) and externally validated in the replication set (0.937; CI:0.926-0.947, n=1948) and the asymptomatic set (0.909; CI:0.879-0.938, n=754). Selecting a cut-off value of 0.30 in the training set resulted in a sensitivity/specificity of 100/78, >99/84, 98/82% in the validation, replication and asymptomatic set, respectively.InterpretationeNose represents a quick and non-invasive method to reliably rule out SARS-CoV-2 infection in public health test facilities and can be used as a screening test to define who needs an additional confirmation test.FundingMinistry of Health, Welfare and SportResearch in contextEvidence before this studyElectronic nose technology is an emerging diagnostic tool for diagnosis and phenotyping of a wide variety of diseases, including inflammatory respiratory diseases, lung cancer, and infections.As of Feb 13, 2021, our search of PubMed using keywords “COVID-19” OR “SARS-CoV-2” AND “eNose” OR “electronic nose” OR “exhaled breath analysis” yielded 4 articles (1-4) that have assessed test characteristics of electronic nose to diagnose COVID-19. In these small studies the obtained signals using sensor-based technologies, two-dimensional gas chromatography and time-of-flight mass spectrometry, or proton transfer reaction time-of-flight mass spectrometry, provided adequate discrimination between patients with and without COVID-19.Added value of this studyWe prospectively studied the accuracy of exhaled breath analysis by electronic nose (eNose) to diagnose or rule out a SARS-CoV-2 infection in individuals with and without symptoms presenting to a public test facility. In the training set with 904 individuals, the eNose sensors were combined into a composite biomarker with a ROC-AUC of 0.948. In three independent validation cohorts of 3606 individuals in total, eNose was able to reliably rule out SARS-CoV-2 infection in 70-75% of individuals, with a sensitivity ranging between 98-100%, and a specificity between 78-84%. No association was found between cycle thresholds values, as semi-quantitative measure of viral load, and eNose variables.Implications of all the available evidenceThe available findings, including those from our study, support the use of eNose technology to distinguish between individuals with and without a SARS-CoV-2 infection with high accuracy. Exhaled breath analysis by eNose represents a quick and non-invasive method to reliably rule out a SARS-CoV-2 infection in public health test facilities. The results can be made available within seconds and can therefore be used as screening instrument. The eNose can reliably rule out a SARS-CoV-2 infection, eliminating the need for additional time-consuming, stressful, and expensive diagnostic tests in the majority of individuals.

Exhaled Breath Analysis for Assessment of IBD (Inflammatory Bowel Disease) Based on Electronic Nose System

2020 ◽  
Vol MA2020-01 (34) ◽  
pp. 2407-2407
Author(s):  
Hyung-Gi Byun ◽  
Joon-Bu Yu ◽  
Chong-Yun Kang ◽  
Yoo-Jin Lee ◽  
Byung-Kuk Jang ◽  
...  
Keyword(s):  
Inflammatory Bowel Disease ◽  
Electronic Nose ◽  
Bowel Disease ◽  
Breath Analysis ◽  
Exhaled Breath ◽  
Exhaled Breath Analysis ◽  
Inflammatory Bowel ◽  
Electronic Nose System

Exhaled Breath Analysis System based on Electronic Nose Techniques Applicable to Lung Diseases

2014 ◽  
Vol 34 (3) ◽  
pp. 125 ◽  
Author(s):  
Hyung-Gi Byun ◽  
Joon-Boo Yu ◽  
Jeung-Soo Huh ◽  
Jeong-Ok Lim
Keyword(s):  
Electronic Nose ◽  
Lung Diseases ◽  
Breath Analysis ◽  
Exhaled Breath ◽  
Exhaled Breath Analysis ◽  
Analysis System

Exhaled breath analysis with electronic nose technology for detection of acute liver failure in rats

2014 ◽  
Vol 53 ◽  
pp. 129-134 ◽  
Author(s):  
K.A. Wlodzimirow ◽  
A. Abu-Hanna ◽  
M.J. Schultz ◽  
M.A.W. Maas ◽  
L.D.J. Bos ◽  
...  
Keyword(s):  
Liver Failure ◽  
Acute Liver Failure ◽  
Electronic Nose ◽  
Breath Analysis ◽  
Exhaled Breath ◽  
Exhaled Breath Analysis ◽  
Electronic Nose Technology

scholarly journals Identification of recent exacerbations in COPD patients by electronic nose

2020 ◽  
Vol 6 (4) ◽  
pp. 00307-2020
Author(s):  
Job J.M.H. van Bragt ◽  
Paul Brinkman ◽  
Rianne de Vries ◽  
Susanne J.H. Vijverberg ◽  
Els J.M. Weersink ◽  
...  
Keyword(s):  
Electronic Nose ◽  
Inhaled Corticosteroids ◽  
Ambient Air ◽  
Breath Analysis ◽  
High Accuracy ◽  
Exhaled Breath ◽  
Molecular Fingerprint ◽  
Linear Discriminant ◽  
Copd Patients ◽  
Exhaled Breath Analysis

Molecular profiling of exhaled breath by electronic nose (eNose) might be suitable as a noninvasive tool that can help in monitoring of clinically unstable COPD patients. However, supporting data are still lacking. Therefore, as a first step, this study aimed to determine the accuracy of exhaled breath analysis by eNose to identify COPD patients who recently exacerbated, defined as an exacerbation in the previous 3 months.Data for this exploratory, cross-sectional study were extracted from the multicentre BreathCloud cohort. Patients with a physician-reported diagnosis of COPD (n=364) on maintenance treatment were included in the analysis. Exacerbations were defined as a worsening of respiratory symptoms requiring treatment with oral corticosteroids, antibiotics or both. Data analysis involved eNose signal processing, ambient air correction and statistics based on principal component (PC) analysis followed by linear discriminant analysis (LDA).Before analysis, patients were randomly divided into a training (n=254) and validation (n=110) set. In the training set, LDA based on PCs 1–4 discriminated between patients with a recent exacerbation or no exacerbation with high accuracy (receiver operating characteristic (ROC)–area under the curve (AUC)=0.98, 95% CI 0.97–1.00). This high accuracy was confirmed in the validation set (AUC=0.98, 95% CI 0.94–1.00). Smoking, health status score, use of inhaled corticosteroids or vital capacity did not influence these results.Exhaled breath analysis by eNose can discriminate with high accuracy between COPD patients who experienced an exacerbation within 3 months prior to measurement and those who did not. This suggests that COPD patients who recently exacerbated have their own exhaled molecular fingerprint that could be valuable for monitoring purposes.

Non-invasive prediction of lung cancer histological types through exhaled breath analysis by UV-irradiated electronic nose and GC/QTOF/MS

2020 ◽  
Vol 311 ◽  
pp. 127932 ◽  
Author(s):  
Tarik Saidi ◽  
Mohammed Moufid ◽  
Kelvin de Jesus Beleño-Saenz ◽  
Tesfalem Geremariam Welearegay ◽  
Nezha El Bari ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Electronic Nose ◽  
Breath Analysis ◽  
Exhaled Breath ◽  
Histological Types ◽  
Non Invasive ◽  
Exhaled Breath Analysis
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