Implementation of Machine Learning for Breath Collection

Paulo Santos, Valentina Vassilenko, Fábio Vasconcelos, Flávio Gil

Abstract

Economic and technologic progresses states the analysis of human’s exhaled air as a promising tool for medical diagnosis and therapy monitoring. Challenges of most pulmonary breath acquisition devices are related to the substances’ concentrations that are source (oral cavity, esophageal and alveolar) dependent and their low values (in ppbv - pptv range). We introduce a prototype that is capable of collecting samples of exhaled air according to the respiratory source and independent of the metabolic production of carbon dioxide. It also allows to access the breathing cycle in real-time, detects the optimized sampling instants and selects the collection pathway through the implementation of an algorithm containing a machine learning process. A graphical interface allows the interaction between the operator/user and the process of acquisition making it easy, quick and reliable. The imposition of breath rhythm led to improvements in accuracy of obtaining samples from specific parts of the respiratory tract and it should be adapted according to their age and physiological/health condition. The technology implemented in the proposed system should be taken into consideration for further studies, since the prototype is suitable for selectively sampling exhaled air from persons according to its age, genre and physiological condition.

References

  1. Miekisch, W, Schubert, JK & Noeldge-Schomburg, GFE 2004, 'Diagnostic potential of breath analysis - focus on volatile organic compounds', Clinica Chimica Acta, vol. 347, pp. 25-39.
  2. Baumbach, JI 2009, 'Ion mobility spectrometry coupled with multi-capillary columns for metabolic profiling of human breath', J. Breath Research, vol. 3, pp. 16.
  3. Di Francesco, F, Fuoco, R, Trivella, MG & Ceccarini, A 2005, 'Breath analysis: trends in techniques and clinical applications', Microchemical Journal, vol. 79, pp. 405- 410.
  4. Manolis, A 1983, 'The diagnostic potencial of breath analysis', Clinical Chemistry, vol. 29, pp. 5-15.
  5. Miekisch, W & Schubert, JK 2006, 'From highly sophisticated analytical techniques to life-saving diagnostics: Technical developments in breath analysis', Trac-Trends in Analyt. Chem., vol. 25, pp. 665-673.
  6. Amann, A, Spanel, P & Smith, D 2007, 'Breath analysis: the approach towards clinical applications', Mini reviews in medicinal chemistry, vol. 7, pp. 115-129.
  7. Dweik, RA & Amann, A 2008, 'Exhaled breath analysis: the new frontier in medical testing', Journal of Breath Research, vol. 2, no. 3, 030301.
  8. Lourenço, C & Turner, C 2014, 'Breath analysis in disease diagnosis: methodological considerations and applications', Metabolites, vol. 4, pp. 465-498.
  9. Mazzatenta, A, Di Giulio, C & Pokorski, M 2013, 'Pathologies currently identified by exhaled biomarkers', Respiratory Physiology & Neurobiology, vol. 187, pp. 128-134.
  10. Phillips, M, Herrera, J, Krishnan, S, Zain, M, Greenberg, J & Cataneo, R 1999, 'Variation in volatile organic compounds in the breath of normal humans', Journal of Chromatography B: Biomedical Science and Applications, vol. 729, pp. 75-88.
  11. Di Natale, C, Paolesse, R, Martinelli, E & Capuano, R 2014, 'Solid-state gas sensors for breath analysis: A review', Analytica Chimica Acta, vol. 824, pp. 1-17.
  12. Ruzsanyi, V 2013, 'Ion mobility spectrometry for pharmacokinetic studies-exemplary application', Journal of Breath Research, vol. 7, no. 4, 046008.
  13. Beauchamp, J 2015, 'Current sampling and analysis techniques in breath research - results of a task force poll', Journal of Breath Research, vol. 9, 047107.
  14. Alonso, M & Sanchez, JM 2013, 'Analytical challenges in breath analysis and its application to exposure monitoring', Trends in Analytical Chemistry, vol. 44, pp. 78-89.
  15. Basanta, M, Koimtzis, T, Singh, D, Wilson, I & Thomas, CL 2007, 'An adaptive breath sampler for use with human subjetcs with an impaired respiratory function', Analyst, vol. 132, no. 2, pp. 153-163.
  16. Droz, PO & Guillemin MP 1986, 'Occupational exposure monitoring using breath analysis', J. Occup. Med., vol. 28, no. 8, pp. 593-602.
  17. Risby, TH 2008, 'Critical issues for breath analysis', Journal of Breath Research, vol. 2, 030302.
  18. Bhavanishankar, K, Kumar, AY, Moseley, HSL & Ahyeehallsworth, R 1995, 'Terminology and the current limitations of time capnography - A brief review', Journal of Clinical Monitoring, vol. 11, pp. 175-82.
  19. Mimoz, O, Benard, T, Gaucher, A, Frasca, D & Debaene, B 2012, 'Accuracy of respiratory rate monitoring using a non-invasive acoustic method after general anaesthesia', Br. J. Anaesth, vol. 108, pp. 872-875.
  20. Bhavanishankar, K, Moseley, H, Kumar, AY & Delph, Y 1992, 'Capnometry and Anesthesia', Canadian Journal of Anaesthesia, vol. 39, pp. 617-632.
  21. Kodali, BS 2013, 'Capnography outside the operating rooms', Anesthesiology, vol. 118, pp. 192-201.
  22. Bhavani-Shankar, K & Philip, JH 2000, 'Defining segments and phases of a time capnogram', Anesth. Analg., vol. 91, no. 4, pp. 973-977.
  23. Dias, F, Alves, J, Januário, F, Ferreira, JL & Vassilenko, V 2013, 'Prototype and Graphical Interface for Selective Exhaled Air Acquisition', Proc. of the Intern. Conf. on Biomedical Electronics and Devices, vol. 1, pp. 216- 219.
  24. Roussos, C & Zakynthinos, S 1996, 'Fatigue of the respiratory muscles', Intensive Care Med., vol. 154, pp. 1099-1105.
  25. Capnia, Inc. 2015, 'Selection, segmentation and analysis of exhaled breath for airway disorders assessment', WO2015143384 A1.
Download


Paper Citation


in Harvard Style

Santos P., Vassilenko V., Vasconcelos F. and Gil F. (2017). Implementation of Machine Learning for Breath Collection . In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIODEVICES, (BIOSTEC 2017) ISBN 978-989-758-216-5, pages 163-170. DOI: 10.5220/0006168601630170


in Bibtex Style

@conference{biodevices17,
author={Paulo Santos and Valentina Vassilenko and Fábio Vasconcelos and Flávio Gil},
title={Implementation of Machine Learning for Breath Collection},
booktitle={Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIODEVICES, (BIOSTEC 2017)},
year={2017},
pages={163-170},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006168601630170},
isbn={978-989-758-216-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIODEVICES, (BIOSTEC 2017)
TI - Implementation of Machine Learning for Breath Collection
SN - 978-989-758-216-5
AU - Santos P.
AU - Vassilenko V.
AU - Vasconcelos F.
AU - Gil F.
PY - 2017
SP - 163
EP - 170
DO - 10.5220/0006168601630170