Design and development of an e-nose system for the diagnosis of pulmonary diseases

Binson, V A; Subramoniam, M.

doi:10.37190/abb-01737-2020-03

Cited by 33 publications

(15 citation statements)

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“…Electronic noses are used in a variety of fields including food technology, chemical analysis, and diagnosis of various diseases like lung cancer, diabetes mellitus, COPD, respiratory infections, cystic fibrosis, asthma, and malignant pleural mesothelioma [12][13][14][15][28][29][30][31][32][33]. The application of e-nose in the prediction of respiratory diseases is also an area of current research.…”

Section: Discussionmentioning

confidence: 99%

“…Different diseases show different symptoms and the changes in breath VOCs are a great sign of pulmonary diseases [12][13][14][15]. There are significant changes in the breath signature for patients with pulmonary diseases like asthma, COPD, cystic fibrosis, and lung cancer.…”

Section: Discussionmentioning

confidence: 99%

“…In India, the major cause of lung cancer and COPD is tobacco smoking both as cigarette and beedi [9]. Both of these diseases can be diagnosed with different MOS sensor arrays using the breath analysis technique [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

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Discrimination of COPD and lung cancer from controls through breath analysis using a self-developed e-nose

2021

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This work details the application of a metal oxide semiconductor (MOS) sensor based electronic nose (e-nose) system in the discrimination of lung cancer and chronic obstructive pulmonary disease (COPD) from healthy controls. The sensor array integrated with supervised classification algorithms was able to detect and classify exhaled breath samples from healthy controls, patients with COPD, and lung cancer by recognizing the amount of volatile organic compounds present in it. This paper details the e-nose design, participant selection, sampling methods, and data analysis. The clinical feasibility of the system was checked in 32 lung cancer patients, 38 COPD patients, and 72 healthy controls including smokers and non-smokers. One of the advantages of the equipment design was portability and robustness since the system was conditioned with elements that allowed its easy movement. In the discrimination of lung cancer from controls, the k-nearest neighbors gave an acceptable accuracy, sensitivity, and specificity of 91.3%, 84.4%, and 94.4% respectively. The support vector machine gave better results for COPD discrimination from controls with 90.9% accuracy, 81.6% sensitivity, and 95.8% specificity. Even though the attained results were good, further examinations are essential to enhance the sensor array system, investigate the long-run reproducibility, repeatability, and enlarge its relevancy.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Discrimination of COPD and lung cancer from controls through breath analysis using a self-developed e-nose

2021

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“…MOS have been used as components of such sensing systems. Arrays of commercial MOS sensors (TGS), have been used by Binson et al [ 86 , 87 ] and Marzorati et al [ 88 ] for the differentiation of LC [ 86 , 87 , 88 ] and COPD patients from HC, with high accuracies ( Table 2 ). Interestingly, LC diagnosis using an array of TGS sensors has been reported in the literature; notably, 85 real-world breath samples of LC patients have been used, aiming not only at the discrimination between LC and HC but also at before- and after-surgery patients [ 89 ].…”

Section: Types Of Nanomaterial-based Sensors In Breath Analysismentioning

confidence: 99%

Breath Analysis: A Promising Tool for Disease Diagnosis—The Role of Sensors

Kaloumenou

Skotadis

Lаgopati

et al. 2022

Sensors

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Early-stage disease diagnosis is of particular importance for effective patient identification as well as their treatment. Lack of patient compliance for the existing diagnostic methods, however, limits prompt diagnosis, rendering the development of non-invasive diagnostic tools mandatory. One of the most promising non-invasive diagnostic methods that has also attracted great research interest during the last years is breath analysis; the method detects gas-analytes such as exhaled volatile organic compounds (VOCs) and inorganic gases that are considered to be important biomarkers for various disease-types. The diagnostic ability of gas-pattern detection using analytical techniques and especially sensors has been widely discussed in the literature; however, the incorporation of novel nanomaterials in sensor-development has also proved to enhance sensor performance, for both selective and cross-reactive applications. The aim of the first part of this review is to provide an up-to-date overview of the main categories of sensors studied for disease diagnosis applications via the detection of exhaled gas-analytes and to highlight the role of nanomaterials. The second and most novel part of this review concentrates on the remarkable applicability of breath analysis in differential diagnosis, phenotyping, and the staging of several disease-types, which are currently amongst the most pressing challenges in the field.

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“…Binson Abraham et al developed an electronic nose system based on the SVM classification algorithm to distinguish 27 patients with lung cancer and 22 patients with chronic obstructive pulmonary disease. The best classification accuracy reached 88.79% [4]. Carmen Bax et al created an electronic nose system to detect respiratory failure in patients infected with the COVID-19 virus.…”

Section: Introductionmentioning

confidence: 99%

Metabolic changes of VOCs in human caused by wearing masks: a preliminary study based on electronic nose

Yang

Xiang

Meng

et al. 2022

International Conference on Biomedical and Intelligent Systems (IC-BIS 2022)

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Wearing masks has been generally recommended to reduce the spreading of COVID-19. However, little is known about its effects on metabolic VOC changes in human body. To explore how the duration of wearing masks influences VOC metabolism in the human body, the essay used a self-developed electronic nose to analyse exhaled breath samples from 10 healthy individuals in this study. Firstly, polytetrafluoroethylene sampling bags are used to collect breath samples after volunteers wearing masks for 1h, 2h, 3h, 4h, and 5h. Secondly, data pre-processing, including baseline calibration and normalization are carried out. Thirdly, the study used LDA for dimensionality reduction on the original data to extract 4 features. Fourthly, differences in the length of time of wearing masks are analysed. Then, 4 algorithms were applied for cluster analysis based on extracted features. Moreover, 3 supervised classification algorithms were used to recognize the duration of wearing masks. Finally, multi-dimensional linear regression is used to study the possibility of predicting the duration of wearing masks based on breath signals acquired through electronic noses. As a result, the first feature extracted by LDA significantly differs from each other in the duration of wearing masks (p<0.05). Cluster analysis results show that the optimal internal parameters Adjusted Rand Index, Adjusted Mutual Information, Homogeneity and V-measure reach 80.2%, 81.5%, 83.5% and 83.7% respectively. Using 5-fold cross-validation on the K nearest neighbour classification model, the best accuracy of recognizing durations of wearing a mask reaches 88%. R-square of multi-dimensional linear regression reaches 92.5%, which shows excellent fitting performance. It can be concluded that the VOC metabolism of the human may change with the duration of wearing masks. Further, “breath prints” obtained by electronic nose may have the potential to predict the effective time and even the quality of masks.

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Design and development of an e-nose system for the diagnosis of pulmonary diseases

Cited by 33 publications

References 0 publications

Discrimination of COPD and lung cancer from controls through breath analysis using a self-developed e-nose

Discrimination of COPD and lung cancer from controls through breath analysis using a self-developed e-nose

Breath Analysis: A Promising Tool for Disease Diagnosis—The Role of Sensors

Metabolic changes of VOCs in human caused by wearing masks: a preliminary study based on electronic nose

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