Review of the algorithms used in exhaled breath analysis for the detection of diabetes

2022 ◽  
Author(s):  
Anna Paleczek ◽  
Artur Maciej Rydosz
Keyword(s):  
Gas Sensors ◽  
Support Vector ◽  
Exhaled Breath ◽  
Diagnostic Systems ◽  
Exhaled Breath Analysis ◽  
Potential Biomarker ◽  
Exhaled Air ◽  
Medical Diagnostic ◽  
Detection Of Biomarkers ◽  
Intensive Work

Abstract Currently, intensive work is underway on the development of truly noninvasive medical diagnostic systems, including respiratory analysers based on the detection of biomarkers of several diseases including diabetes. In terms of diabetes, acetone is considered as a one of the potential biomarker, although is not the single one. Therefore, the selective detection is crucial. Most often, the analysers of exhaled breath are based on the utilization of several commercially available gas sensors or on specially designed and manufactured gas sensors to obtain the highest selectivity and sensitivity to diabetes biomarkers present in the exhaled air. An important part of each system are the algorithms that are trained to detect diabetes based on data obtained from sensor matrices. The prepared review of the literature showed that there are many limitations in the development of the versatile breath analyser, such as high metabolic variability between patients, but the results obtained by researchers using the algorithms described in this paper are very promising and most of them achieve over 90% accuracy in the detection of diabetes in exhaled air. This paper summarizes the results using various measurement systems, feature extraction and feature selection methods as well as algorithms such as Support Vector Machines, k-Nearest Neighbours and various variations of Neural Networks for the detection of diabetes in patient samples and simulated artificial breath samples.

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.

Optical Gas Sensors for Exhaled Breath Analysis

2017 ◽  
Author(s):  
Paula Regina Fortes ◽  
João Flávio da Silveira Petruci ◽  
Ivo Milton Raimundo
Keyword(s):  
Gas Sensors ◽  
Breath Analysis ◽  
Exhaled Breath ◽  
Exhaled Breath Analysis

Metal oxide gas sensors for exhaled breath analysis: a review

2021 ◽  
Author(s):  
Daejeong Yang ◽  
Ramu Adam Gopal ◽  
Telmenbayar Lkhagvaa ◽  
Dongjin Choi
Keyword(s):  
Metal Oxide ◽  
Gas Sensors ◽  
Breath Analysis ◽  
Exhaled Breath ◽  
Exhaled Breath Analysis ◽  
Metal Oxide Gas Sensors

scholarly journals Artificial Breath Classification Using XGBoost Algorithm for Diabetes Detection

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4187
Author(s):  
Anna Paleczek ◽  
Dominik Grochala ◽  
Artur Rydosz
Keyword(s):  
Medical Diagnosis ◽  
Data Interpretation ◽  
Breath Analysis ◽  
Exhaled Breath ◽  
Exhaled Breath Analysis ◽  
Low Concentrations ◽  
Exhaled Air ◽  
Novel Algorithms

Exhaled breath analysis has become more and more popular as a supplementary tool for medical diagnosis. However, the number of variables that have to be taken into account forces researchers to develop novel algorithms for proper data interpretation. This paper presents a system for analyzing exhaled air with the use of various sensors. Breath simulations with acetone as a diabetes biomarker were performed using the proposed e-nose system. The XGBoost algorithm for diabetes detection based on artificial breath analysis is presented. The results have shown that the designed system based on the XGBoost algorithm is highly selective for acetone, even at low concentrations. Moreover, in comparison with other commonly used algorithms, it was shown that XGBoost exhibits the highest performance and recall.

scholarly journals Assessment of Electronic Sensing Techniques for the Rapid Identification of Alveolar Echinococcosis through Exhaled Breath Analysis

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2666 ◽  
Author(s):  
Andrzej Kwiatkowski ◽  
Tomasz Chludziński ◽  
Tarik Saidi ◽  
Tesfalem Geremariam Welearegay ◽  
Aylen Lisset Jaimes-Mogollón ◽  
...  
Keyword(s):  
Success Rate ◽  
Gas Sensors ◽  
Alveolar Echinococcosis ◽  
Early Stage ◽  
Function Analysis ◽  
Breath Analysis ◽  
Rapid Identification ◽  
Exhaled Breath ◽  
Exhaled Breath Analysis ◽  
Sensors Array

Here we present a proof-of-concept study showing the potential of a chemical gas sensors system to identify the patients with alveolar echinococcosis disease through exhaled breath analysis. The sensors system employed comprised an array of three commercial gas sensors and a custom gas sensor based on WO3 nanowires doped with gold nanoparticles, optimized for the measurement of common breath volatile organic compounds. The measurement setup was designed for the concomitant measurement of both sensors DC resistance and AC fluctuations during breath samples exposure. Discriminant Function Analysis classification models were built with features extracted from sensors responses, and the discrimination of alveolar echinococcosis was estimated through bootstrap validation. The commercial sensor that detects gases such as alkane derivatives and ethanol, associated with lipid peroxidation and intestinal gut flora, provided the best classification (63.4% success rate, 66.3% sensitivity and 54.6% specificity) when sensors’ responses were individually analyzed, while the model built with the AC features extracted from the responses of the cross-reactive sensors array yielded 90.2% classification success rate, 93.6% sensitivity and 79.4% specificity. This result paves the way for the development of a noninvasive, easy to use, fast and inexpensive diagnostic test for alveolar echinococcosis diagnosis at an early stage, when curative treatment can be applied to the patients.

scholarly journals Sensors for Enhanced Detection of Acetone as a Potential Tool for Noninvasive Diabetes Monitoring

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2298 ◽  
Author(s):  
Artur Rydosz
Keyword(s):  
Gas Sensors ◽  
Medical Diagnosis ◽  
Point Of View ◽  
Exhaled Breath ◽  
Promising Tool ◽  
Exhaled Breath Analysis ◽  
Volatile Organic ◽  
Diabetes Monitoring ◽  
Acetone Detection ◽  
Potential Tool

Measurement of blood-borne volatile organic compounds (VOCs) occurring in human exhaled breath as a result of metabolic changes or pathological disorders is a promising tool for noninvasive medical diagnosis, such as exhaled acetone measurements in terms of diabetes monitoring. The conventional methods for exhaled breath analysis are based on spectrometry techniques, however, the development of gas sensors has made them more and more attractive from a medical point of view. This review focuses on the latest achievements in gas sensors for exhaled acetone detection. Several different methods and techniques are presented and discussed as well.

Optical Gas Sensors for Exhaled Breath Analysis

2017 ◽  
Author(s):  
Paula Regina Fortes ◽  
João Flávio da Silveira Petruci ◽  
Ivo Milton Raimundo
Keyword(s):  
Gas Sensors ◽  
Breath Analysis ◽  
Exhaled Breath ◽  
Exhaled Breath Analysis
Sign in / Sign up

Export Citation Format

Share Document