Please wait a minute...

Frontiers of Optoelectronics

Front. Optoelectron.    2015, Vol. 8 Issue (2) : 183-186
Diagnostics of bronchopulmonary diseases through Mahalanobis distance-based absorption spectral analysis of exhaled air
1. Siberian State Medical University, Tomsk 634050, Russia
2. Tomsk State University, Tomsk 634050, Russia
3. V.E. Zuev Institute of Atmospheric Optics SB RAS, Tomsk 634055, Russia
Download: PDF(183 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Accurate diagnosis of different bronchopulmonary diseases is important in clinical practice. This study involved 20 healthy volunteers and 77 patients with bronchopulmonary diseases, including chronic obstructive pulmonary disease (COPD), bronchial asthma, pulmonary tuberculosis, and community-acquired pneumonia. The absorption spectrum of exhaled air samples was recorded on an intra-cavity photo-acoustic gas analyzer (ILPA-1, Special Technologies, Ltd., Russia) with photo-acoustic detectors and CO2 laser with a tuning range from 9.2 to 10.8 μm. In conclusion, analysis of the Mahalanobis distance-based absorption spectral profiles of breath air from bronchopulmonary patients and healthy volunteers allows the formulation of a preliminary diagnosis.

Keywords bronchopulmonary diseases      exhaled air      Mahalanobis distance      laser photo-acoustic spectroscopy      CO2 laser     
Corresponding Author(s): A. A. BULANOVA   
Just Accepted Date: 15 May 2015   Issue Date: 24 June 2015
 Cite this article:   
A. A. BULANOVA,E. B. BUKREEVA,Yu. V. KISTENEV, et al. Diagnostics of bronchopulmonary diseases through Mahalanobis distance-based absorption spectral analysis of exhaled air[J]. Front. Optoelectron., 2015, 8(2): 183-186.
E-mail this article
E-mail Alert
Articles by authors
group gender and number age total number in the group
male female
healthy volunteers 5 15 26.90±6.96 20
patients with COPD 27 4 61.90±8.14 31
patients with bronchial asthma 3 13 59.30±12.85 16
patients with tuberculosis 8 2 60.0±5.67 10
patients with pneumonia 10 10 41.85±17.60 20
Tab.1  Information about the groups
parameter healthy volunteers patients with ULD patients with bronchial asthma patients with COPD p value
1 2 3 4
N median (25%-75%) N median (25%-75%) N median (25%-75%) N median (25%-75%) p12 p13 p14 p23 p24 p34
I1 20 1.11(0,86-1,32) 30 3.96(2,59-28,33) 16 3.37(2,30-6,45) 31 1.56(1,18-2,26) 0.001 0.001 0.002 1 0.001 0.006
I2 20 1.03(0.86-1.38) 30 2.81(1.86-4.71) 16 2.61(1.90-4.28) 31 1.26(1.09-1.82) 0.001 0.001 0.097 1 0.001 0.001
Tab.2  Values of I 1 , I 2 in the groups
pairwise classification threshold value of I1 target disease sensitivity (Se)/% specificity (Sp)/%
healthy volunteers – patients with ULD ≥1.74 ULD 90 90
healthy volunteers – patients with bronchial asthma ≥1.66 bronchial asthma 90 81
healthy volunteers – patients with COPD ≥1.28 COPD 70 70
patients with ULD – patients with COPD ≤2.45 COPD 80 80
patients with bronchial asthma – patients with COPD ≤2.29 COPD 75 74
Tab.3  Diagnostic intervals of I1, sensitivity, and specificity of the method
1 Bukreeva E B, Bulanova A A, Kistenev Y V, Kuzmin D A, Tuzikov S A, Yumov E L. Analysis of the absorption spectra of gas emission of patients with lung cancer and chronic obstructive pulmonary disease by laser optoacoustic spectroscopy. In: Proceedings of SPIE 8699, Saratov Fall Meeting 2012: Optical Technologies in Biophysics and Medicine XIV; and Laser Physics and Photonics XIV. 2013, 86990K
2 Bessa V, Darwiche K, Teschler H, Sommerwerck U, Rabis T, Baumbach J I, Freitag L. Detection of volatile organic compounds (VOCs) in exhaled breath of patients with chronic obstructive pulmonary disease (COPD) by ion mobility spectrometry. International Journal for Ion Mobility Spectrometry, 2011, 14(1): 7-13
3 Van Berkel J J B N, Dallinga J W, M?ller G M, Godschalk R W L, Moonen E J, Wouters E F M, Van Schooten F J. A profile of volatile organic compounds in breath discriminates COPD patients from controls. Respiratory Medicine, 2010, 104(4): 557-563
4 Phillips C O, Syed Y, Parthaláin N M, Zwiggelaar R, Claypole T C, Lewis K E. Machine learning methods on exhaled volatile organic compounds for distinguishing COPD patients from healthy controls. Journal of Breath Research, 2012, 6(3): 036003 pmid: 22759349
5 Boshier P R, Mistry V, Cushnir J R, Curtis S, Elkin S, Kon O M, Marczin N, Hanna G B. Analysis of volatile biomarkers within exhaled breath for the diagnosis of pneumonia. Thorax, 2010, 65(Suppl 4): A58-A59
6 Ageev B G, Kistenjov J V, Nekrasov E V, Nikiforova O J, Nikotin E S, Nikotina G S, Ponomarjov J N, Urazova O I, Filinjuk O V, Fokin V A, Janova G V. Estimate of expired air samples of patients with the pulmonary tuberculosis using laser photoacoustic spectroscopy technique. Bulletin of Siberian Medicine, 2012, 4: 116-120
7 Kharitonov S A, Barnes P J. Exhaled markers of pulmonary disease. American Journal of Respiratory and Critical Care Medicine, 2001, 163(7): 1693-1722 pmid: 11401895
8 Intracavity laser opto-acoustic sensor ILPA-1. Passport. Technical description. Operating Instructions. Special Technologies, Ltd, Russia, Novosibirsk
9 Kistenev Y V, ed. Applications of laser spectroscopy and nonlinear analysis methods for investigation of medical-biological objects. Tomsk: TPU Ed., 2007
Full text