125 μm-breath sensor with high sensitivity and rapid response was prepared by using n-type Si: Au material. Its sensitivity coefficient and time constant were 4 V.sec / L and 38 msec, respec-tively. Its working princ...125 μm-breath sensor with high sensitivity and rapid response was prepared by using n-type Si: Au material. Its sensitivity coefficient and time constant were 4 V.sec / L and 38 msec, respec-tively. Its working principle was based on ano- malous resistance effect, which not only increa- sed the sensitivity, but also reduced its time con-stant greatly. Its signal processing system can select the breath signals and work stably. Therefore, the small changes of breath system can be measured and, especially, patient’s breath rate can be monitored at a distance.展开更多
The increasing morbidity of internal diseases poses serious threats to human health and quality of life.Exhaled breath analysis is a noninvasive and convenient diagnostic method to improve the cure rate of patients. I...The increasing morbidity of internal diseases poses serious threats to human health and quality of life.Exhaled breath analysis is a noninvasive and convenient diagnostic method to improve the cure rate of patients. In this study, a self-powered breath analyzer based on polyaniline/polyvinylidene fluoride(PANI/PVDF) piezogas-sensing arrays has been developed for potential detection of several internal diseases. The device works by converting exhaled breath energy into piezoelectric gassensing signals without any external power sources. The five sensing units in the device have different sensitivities to various gas markers with concentrations ranging from 0 to 600 ppm. The working principle can be attributed to the coupling of the in-pipe gas-flow-induced piezoelectric effect of PVDF and gas-sensing properties of PANI electrodes. In addition, the device demonstrates its use as an ethanol analyzer to roughly mimic fatty liver diagnosis.This new approach can be applied to fabricating new exhaled breath analyzers and promoting the development of self-powered systems.展开更多
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 a...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.展开更多
文摘125 μm-breath sensor with high sensitivity and rapid response was prepared by using n-type Si: Au material. Its sensitivity coefficient and time constant were 4 V.sec / L and 38 msec, respec-tively. Its working principle was based on ano- malous resistance effect, which not only increa- sed the sensitivity, but also reduced its time con-stant greatly. Its signal processing system can select the breath signals and work stably. Therefore, the small changes of breath system can be measured and, especially, patient’s breath rate can be monitored at a distance.
基金supported by the National Natural Science Foundation of China (11674048)the Fundamental Research Funds for the Central Universities (N170505001 and N160502002)Program for Shenyang Youth Science and Technology Innovation Talents (RC170269)
文摘The increasing morbidity of internal diseases poses serious threats to human health and quality of life.Exhaled breath analysis is a noninvasive and convenient diagnostic method to improve the cure rate of patients. In this study, a self-powered breath analyzer based on polyaniline/polyvinylidene fluoride(PANI/PVDF) piezogas-sensing arrays has been developed for potential detection of several internal diseases. The device works by converting exhaled breath energy into piezoelectric gassensing signals without any external power sources. The five sensing units in the device have different sensitivities to various gas markers with concentrations ranging from 0 to 600 ppm. The working principle can be attributed to the coupling of the in-pipe gas-flow-induced piezoelectric effect of PVDF and gas-sensing properties of PANI electrodes. In addition, the device demonstrates its use as an ethanol analyzer to roughly mimic fatty liver diagnosis.This new approach can be applied to fabricating new exhaled breath analyzers and promoting the development of self-powered systems.
文摘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.
