Online breath analysis using metal oxide semiconductor sensors (electronic nose) for diagnosis of lung cancer

Abstract

The analysis of exhaled breath is drawing a high degree of interest in the diagnostics of various diseases, including lung cancer. Electronic nose (E-nose) technology is one of the perspective approaches in the field due to its relative simplicity and cost efficiency. The use of an E-nose together with pattern recognition algorithms allow 'breath-prints' to be discriminated. The aim of this study was to develop an efficient online E-nose-based lung cancer diagnostic method via exhaled breath analysis with the use of some statistical classification methods. A developed multisensory system consisting of six metal oxide chemoresistance gas sensors was employed in three temperature regimes. This study involved 118 individuals: 65 in the lung cancer group (cytologically verified) and 53 in the healthy control group. The exhaled breath samples of the volunteers were analysed using the developed E-nose system. The dataset obtained, consisting of the sensor responses, was pre-processed and split into training (70%) and test (30%) subsets. The training data was used to fit the classification models; the test data was used for the estimation of prediction possibility. Logistic regression was found to be an adequate data-processing approach. The performance of the developed method was promising for the screening purposes (sensitivity-95.0%, specificity-100.0%, accuracy-97.2%). This shows the applicability of the gas-sensitive sensor array for the exhaled breath diagnostics. Metal oxide sensors are highly sensitive, low-cost and stable, and their poor sensitivity can be enhanced by integrating them with machine learning algorithms, as can be seen in this study. All experiments were carried out with the permission of the N.N. Petrov Research Institute of Oncology ethics committee no. 15/83 dated

Development of a Compact, IoT-Enabled Electronic Nose for Breath Analysis

“…These reviews demonstrate that MOX sensors are currently under-utilised in E-nose applications for exhaled breath research. While new prototype MOX-based E-noses are currently being developed for diagnostic purposes using exhaled breath [20], these often use custom sensors or traditional commercial MOX sensors/E-noses, which have been available for many years [21]. The latest generation of commercially available MEMS MOX gas sensors have not been sufficiently evaluated for this purpose.…”

Cancer Diagnosis by Neural Network Analysis of Data from Semiconductor Sensors

“…The measured results consisted of vectors of 64 values every 20 s for each of the three sensors. Previous studies, with varying degrees of success, have used the “electronic nose” device to diagnose lung [ 15 , 17 , 25 , 26 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ], colon [ 36 , 37 ], head and neck [ 34 , 37 , 38 ], urinary bladder [ 37 ], prostate [ 39 ], stomach [ 28 ] and breast cancer [ 27 ].…”

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

“…The E-nose system is made up of a MOS [5] , [6] sensor chamber with a capacity of 30 mL, an Arduino nano card to control the electronics, a wireless DAQ card for data acquisition, and a PC where data acquisition software is used and the information is processed and analyzed (See Fig. 2 ).…”