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当前位置: 首页 > 技术文献 > 应用文献 > 用于糖尿病和肺癌患者呼吸分析非侵入性电子鼻诊断技术
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用于糖尿病和肺癌患者呼吸分析非侵入性电子鼻诊断技术

来源: Bhagaban Behera, Rathin Joshi, Anil Vishnu G K, Sanjay Bhalerao,Hardik J. Pandya  发布日期: 2019-07-08  访问量: 102


在人类呼出的呼吸中,发现了3000多种挥发性有机化合物(VOC),它们直接或间接地与人体内部的生化过程有关。通过研究呼吸印记,电子鼻可以在筛选/分析各种呼吸系统疾病中发挥潜在的作用。电子鼻集成传感器阵列和人工神经网络,对特定的VOC模式作出反应,因此可以作为一种无创的疾病监测技术...
标签: Cyranose 320电子鼻、糖尿病、肺癌、呼吸印记、无创技术
 

Electronic-nose: A non-invasive technology for breath analysis of diabetes and lung cancer patients

用于糖尿病和肺癌患者呼吸分析非侵入性电子鼻诊断技术

Bhagaban Behera1, Rathin Joshi1, Anil Vishnu G K2, Sanjay Bhalerao3, 4 and Hardik J. Pandya1,*

1Biomedical and Electronic (10-6-10-9) Engineering Systems Laboratory 6 Department of Electronic Systems Engineering, 7 Indian Institute of Science, Bangalore, India 560012 8
2Center for BioSystems Science and Engineering, 9 Indian Institute of Science, Bangalore, India 560012 10
3Parivartan Healthcare, 102 MIG Colony, Indore, India 452001 11 *Corresponding author: hjpandya@iisc.ac.in

To cite this article before publication: Bhagaban Behera et al 2019 J. Breath Res

This Accepted Manuscript is © 2018 IOP Publishing Ltd.

 

Abstract
In human-exhaled breath, more than 3000 volatile organic compounds (VOCs) are found which  are directly or indirectly related to internal biochemical processes in the body. Electronic noses  (E-noses) could play a potential role in screening/analyzing various respiratory and systemic diseases by studying breath signatures. E-nose integrates sensor array and an artificial neural  network that responds to specific patterns of VOCs and thus can act as a non-invasive  technology for disease monitoring. Gold standard blood glucose monitoring for diabetes diagnostics is invasive and highly uncomfortable. This contributes to the massive need for technologies which are non-invasive and can be used as an alternative to blood measurements  for glucose detection. While lung cancer is one of the deadliest cancers with the highest death rate and an extremely high yearly global burden. The conventional means such as sputum cytology, chest radiography, or computed tomography do not support wide-range population screening. Few standard non-invasive techniques such as mass spectrometry and gas  chromatography are expensive, non-portable, and requires skilled personnel for operation and  are again not suitable for massive screening. Breath contains the markers for both diabetes and  lung cancer along with markers for several diseases and thus, a non-invasive technique like E-nose would greatly improve the analysis procedures over existing invasive methods. This  review shows the state-of-the-art technologies for VOCs detection and machine-learning  approaches for two clinical models: diabetes and lung cancer detection.

在人类呼出的呼吸中,发现了3000多种挥发性有机化合物(VOC),它们直接或间接地与人体内部的生化过程有关。通过研究呼吸印记,电子鼻可以在筛选/分析各种呼吸系统疾病中发挥潜在的作用。电子鼻集成传感器阵列和人工神经网络,对特定的VOC模式作出反应,因此可以作为一种无创的疾病监测技术。用于糖尿病诊断的金标准血糖监测具有侵入性,非常不便。这有助于对非侵入性技术的巨大需求,可作为血糖检测血液测量的替代方法。肺癌是最致命的癌症之一,死亡率最高,每年全球负担极高。传统的手段,如痰液细胞学检查,胸部X光检查,或计算机断层扫描不支持广泛的人群筛选。很少有标准的非侵入性技术,如质谱法和气相色谱法是昂贵的,不便携式的,需要熟练的操作人员,再次不适合大规模筛选。呼吸包含糖尿病和肺癌的标志物以及几种疾病的标志物,因此,像电子鼻这样的非侵入性技术将大大改善现有侵入性方法的分析程序。本文综述了两种临床模型(糖尿病和肺癌检测)中VOCS检测和机器学习方法的最新技术。

Keywords: Electronic-nose, non-invasive technologies, breath signals, volatile organic  compounds, diabetes, lung cancer

关键词:电子鼻;无创技术;呼吸印记;挥发性有机化合物;糖尿病;肺癌

 


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