Electronic Nose for Identification of Lung Diseases

Ogorodnik, V.; Kleperis, Jānis; Taivāns, Immanuels; Jurka, Normunds; Bukovskis, Maris

doi:10.2478/v10047-008-0026-2

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…In 2009, Ogorodnik et al [38] analyzed VOCs from a breath sample of a patient with different lung diseases by using an e-nose with ten MOSFET sensors and four SnO 2 sensors. In total, 66 individuals -23 with asthma, 3 with chronic obstructive pulmonary disease (COPD), 12 with pneumonia, 13 with lung cancer, 4 in the past operation state (removed lung cancer), and 11 healthy volunteers were tested at two different times and ANN analysis was employed to classify the samples of cancer and other lung diseases.…”

Section: Breath Biomarker and Diseasesmentioning

confidence: 99%

Breath Analysis for Detecting Diseases on Respiratory, Metabolic and Digestive System

Kou¹,

Zhang²,

You³

et al. 2019

JBiSE

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Recently, biological technology and computer science are of great importance in medical applications. Since one's breath biomarkers have been proved to be related with diseases, it is possible to detect diseases by analysis of breath samples captured by e-noses. In this paper, a novel medical e-nose system specific to disease diagnosis was used to collect a large-scale breath dataset. Methods for signal processing, feature extracting as well as feature & sensor selection were discussed for detecting diseases on respiratory, metabolic and digestive system. Sequential forward selection is used to select the best combination of sensors and features. The experimental results showed that the proposed system was able to well distinguish healthy samples and samples with different diseases. The results also showed the most significant sensors and features for different tasks, which meets the relationship between diseases and breath biomarkers. By selecting best combination of different sensors and features for different tasks, the e-nose system is shown to be helpful and effective for diseases diagnosis on respiratory, metabolic and digestive system. Open Access https://doi.org/10.4236/jbise.2019.121004 40 J. Biomedical Science and Engineering Breath Analysis with E-NoseCurrently, the measurement of exhaled breath is usually performed by two common gas analysis apparatuses, gas chromatography (GC) [26] or electronic nose (e-nose) [27]. GC can separate and identify molecules that are responsible for typical odors occurring in specific diseases, which is very accurate for

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Section: Breath Biomarker and Diseasesmentioning

confidence: 99%

Breath Analysis for Detecting Diseases on Respiratory, Metabolic and Digestive System

Kou¹,

Zhang²,

You³

et al. 2019

JBiSE

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show abstract

“…Electronic nose system is a simple, non-invasive, accurate diagnostic tool used for analysis of human breath sample for various applications. Several researchers conducted studies in the analysis of human breath using e-nose system for various kinds of disease diagnosis such as diabetes, lung disorders, uremia, liver disorders and chronic kidney disorders (CKD) [16][17][18][19][20][21][22][23]. Lin et al conducted the study using the E-nose system to detect the renal disease using the quartz crystal sensors.…”

Section: Introductionmentioning

confidence: 99%

Design and development of portable prototype for human breath analysis: a comparative study between haemodialysis patients and healthy subjects

Snekhalatha¹,

Nasimsha²,

Kumar³

et al. 2019

Biomed. Phys. Eng. Express

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Certain diseases and clinical conditions alter the exhaled breath gas components. Ammonia was identified as a biomarker for chronic kidney disease (CKD) from the laboratory analytical techniques. Although these techniques were accurate, lack of portability, time consumption and affordablity for the masses were the primary concern for the medical fraternity. To overcome these issues, a portable prototype was developed for the analysis of breath samples from patients undergoing hemodialysis. The aim and objectives of the study was as follows: (i) to detect ammonia in human breath sample using the commercial gas sensor (MQ135); (ii) to acquire breath patterns using NI-myDAQ for preprocessing and extraction of ten transient and steady state features; (iii) To classify the normal and abnormal breath signal using different neural network classifiers (Support Vector Machines, Naïve Bayes, and Multi-layer perceptron). Twenty-one subjects undergoing hemodialysis and 17 healthy subjects were included in the study. The obtained breath patterns revelaed that voltage flag runs between 0.6-0.9 V for a healthy individual, and 1.8-2.65 V for CKD patients. The variations in magnitude voltage were high for CKD subjects compared to healthy subjects. Among the obtained transient and steady-state features, normalized voltage change (ΔV m ) showed a greater % difference between healthy and CKD patients undergoing haemodialysis. The multilayer perceptron achieved a better classification accuracy of 76.31% compared to SVM (60.52%) and naive bayes classifier (55.2%). Hence the developed prototype was simple in operation and cost effective, provides an affordable and act as complimentary diagnostic tool for CKD patients.

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From electrochromic phenomena to energy harvesting and storage—an overview of solid state ionics research at the Institute of Solid State Physics, University of Latvia

Kleperis

Bajārs

Grīnberga

et al. 2023

J Solid State Electrochem

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Electronic Nose for Identification of Lung Diseases

Cited by 3 publications

References 7 publications

Breath Analysis for Detecting Diseases on Respiratory, Metabolic and Digestive System

Breath Analysis for Detecting Diseases on Respiratory, Metabolic and Digestive System

Design and development of portable prototype for human breath analysis: a comparative study between haemodialysis patients and healthy subjects

From electrochromic phenomena to energy harvesting and storage—an overview of solid state ionics research at the Institute of Solid State Physics, University of Latvia

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