Application of electronic nose for rapid diagnosis of tuberculosis

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Application of electronic nose for rapid diagnosis of tuberculosis

  1. 1. Application ofElectronic nose forrapid diagnosis ofTuberculosis Ankit Kumar Singh
  2. 2. Introduction • 9.2 million new cases of TB were reported in 2006 resulting into 1.7 million deaths (WHO report) • Active TB infection occurs when immune system is compromised • Existing techniques are time taking and have low sensitivity • VOC composition of normal breath is affected • Electronic gas sensor array is used for detection
  3. 3. Proposed Idea • Exhaled breath is a type of bodily excretion • To detect the specific pattern of the VOCs in the exhaled breath using Electronic nose • Electronic nose – an array of electronic gas sensors • Signal is analyzed using PCA • Pattern is generated by reducing the dimensions of obtained data • Comparison with the standard
  4. 4. VOC composition of normal andinfected breathA list of 10 most abundant VOCs of M. tuberculosis is given bellow*• Naphthalene• 1-methyl-3-Heptanone• Methylcyclododecane• Heptanes• 2, 2, 4, 6, 6-pentamethyl- Benzene,• 1-methyl-4-(1-methylethyl)-Cyclohexane• 1,4-dimethyl-3,5-dimethylamphetamine• Butanal• 3-methyl- 2-Hexene• Trans-anti-1-methyl-decahydronaphthalene
  5. 5. Electronic Sensors : 3 type of sensors can be used: • Thick Film: Paste is screen printed on alumina substrate Low sensitivity and specificity • Thin Film: Technique used – Chemical Vapor Deposition Uniform layer having higher surface area • MOS – FET: Has high sensitivity, specificity depends on the gate material
  6. 6. Signal analysis (PCA) • To filter noise and reduce the dimensions • Co-variance matrix is calculated from the obtained data • Eigen vectors are calculated for this matrix • Feature vector is obtained by omitting the less significant Eigen vectors • Using this feature vector back calculation is done to obtain new set of data • New data varies only along the selected Eigen vectors
  7. 7. Pattern Analysis: • Pattern obtained is unique to that composition • New samples are compared with a pattern library using pattern recognition algorithms • Self learning is needed to build the pattern library • Result is obtained in terms of percentage similarity • Threshold can be set accordingly
  8. 8. Conclusion:• The test is non invasive hence no contamination issues• Rapid and low cost• High enough sensitivity, can detect TB in early stages• Technique can be developed further to detect lung cancer, asthma, diabetes etc

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