Abstract
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.
Highlights
Traditional diagnosis methods include blood, urine tests and some other methods
Acetone has been found to be more abundant in the breath of diabetics [13, 14], and breath ammonia is significantly higher in patients with renal diseases [15]
The results showed that the e-nose was able to diagnose diabetes with a sensitivity of 77.8% and a specificity of 35.7%
Summary
Traditional diagnosis methods include blood, urine tests and some other methods. Nowadays, biological technology and computer science are playing their roles in medical applications. Acetone has been found to be more abundant in the breath of diabetics [13, 14], and breath ammonia is significantly higher in patients with renal diseases [15] These molecules are considered as biomarkers of the presence of diseases and clinical conditions. Much can be learnt from them about the overall state of an individual’s metabolism or physical condition Compared with these methods, breath analysis has many advantages [16]. Basing on the process of gas exchange in breath, we can see that diseases of respiratory system and metabolic system will strongly affect one’s exhaled breath. We collected a breath analysis dataset with both healthy samples and samples with different kinds of diseases of respiratory, metabolic and digestive system by a specific e-nose system. Experiments were organized on the collected dataset to discover a proper method of breath analysis for disease diagnosis
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