Inventory reference ISSN 1812-7231 Klin.inform.telemed. Volume 12, Issue 13, 2017, Pages 0–00
Author(s) P.F. Shapov1, A.V. Gorbulichy2, R.S. Tomashevsky1, Yu.A. Zaikin2
Institution(s) 1National Technical University "Kharkiv Polytechnic Institute" (NTU "KhPI"), Kharkiv, Ukraine 2Kharkiv Medical Academy of Postgraduate Education (KhMAPO), Kharkiv, Ukraine
Article title Use of parameters of unsteadiness of signals of pulmonary auscultation for detection and localization of pulmonary pathology.
Introduction The paper proposes the use of monitoring of pH in the esophagus in combination with an auscultation of the lungs to assess the effect of gastrointestinal-esophageal-tracheobronchial reflux.
Problem statement. Methodology. A typical auscultatory signal is presented as a quasiperiodic random process and bronchoobstructive changes caused by reflux as a random factor affecting this process. The approach of estimating the step of the influence of this factor is proposed on the basis of comparison of indices of the nonstationarity of the spectrum of the signal itself and its linear transformation. To calculate the spectral-temporal signal parameters, a continuous wavelet transform for the discrete signals was used. The level of significance of the factor influence the informative signal was estimated using T-statistics.
Purpose of work. Increasing the effectiveness of information and measurement technologies for non-invasive express control of the state of respiratory organs in the diagnosis of reflux-associated bronchial asthma.
Research results. As a result of the work, a method for evaluating the effect of reflux on respiratory organs during postoperative recovery was developed and experimentally confirmed. The method consists in the computation of informative indicators that quantify the level of the nonstationarity of the signal and its linear transformation, the coefficients of the inter-spectral correlation. The results of experimental studies confirmed the effectiveness and statistical significance of the proposed method and informative indicators.
Conclusion The task of increasing the reliability of the classification of postoperative pulmonary complications was solved in the work, and the possibility of localization of lung limb obstruction was proved by means of auscultative signal monitoring
The use of the mathematical apparatus presented in the article may prove useful in solving the problem of diagnosis of gastroesophagotracheobronchial reflux and refluxassociated bronchial asthma.
Keywords reflux; bronchial asthma; express control; wavelet transformation; coherence function; nonstationarity
1. Mirskii G.Y. Stochastic Interaction Characteristics and Their Measurements. M., Energoizdat, 1982. 236 p.
2. Bendat J.S.; Piersol A.G. Random Data: Analysis and Measurements Procedures, 4th ed. Wiley, 2010, 640p.
3. Bendat J.S., Piersol, A.G. Engineering Applications of Correlations and Spectral Analysis, 2nd ed. Wiley 1993. 472 p.
4. Gardner W.A. (ed.), Cyclostationarity in Communications and Signal Processing. New York., IEEE press, 1994. 621 p.
5. Napolitano A. Generalizations of Cyclostationarity Signal Processing Spectral Analysisa Applications. Wiley IEEE press, 2012. 492 p.
6. Gardner W. A., Napolitano A., Paura L. Cyclostationarity: half a century of research. Signal processing, 2006, vol. 86, n. 4, pp. 639-697, DOI:10.106/j.sigpro.2005.06.016.
7. Yavorsky J.M. Mathematical models and Analysis of Stochastic Oscillations. Lviv., Karpenko Physico-Mechanical Institute of the NAS of Ukraine, 2013, 187 p.
8. Hinich, M.J. A statistical theory of signal coherence. IEEE J. Oceanic engineering, apr. 2000, vol. 25, n.2, pp. 256-261, DOI: 10.1109/48.838988.
9. Gardner W.A. Introduction to Random Processes with Application to Signals and Systems. NY., Macmillan, 1985. 434 p.
10. Gardner W.A. On the Spectral Coherence of Nonstationary Processes. IEEE trans. Signal process, 1991, vol. 39 n.2, pp. 424-430, doi:10.1109/78.80825.
11. Gardner W.A. Exploitation of Spectral Redundancy in Cyclostationary Signals. IEEE SP Magazine (Signal Processing), apr. 1991, vol. 8, n. 2, pp. 14-36 DOI: 10.1109/79.81007.
12. Hurd H.HL.; Miamme, A. Periodically correlated random sequences. Spectral theory and practice. New Jercey, Wiley-Intersciense, 2007, 353 p.
13. Pollard J. Spravochnik po vyichislitelnyim metodam statistiki [Handbook of computational methods of statistics.]. M.: Finansyi i statistika Publ, 1982. 344 p. (In Rus).
14. Neyronnyie seti dlya oboabotki informatsii [Neural networks for information processing]. M.: Finansyi i statistika Publ., 2002. 344 p. (In Rus).
15. Voskoboynikov Yu.E., Gochakov A.V., Kolker A.B. Filtratsii signalov i izobrazheniy: Fure i veyvlet algoritmyi (s primerami v Mathcad) [Filtering signals and images: Fourier and wavelet algorithms] Novosib. Gos. arhitektur.-stroit. un-t (Sibstrin), 2010,188 p. ISBN 978-5-7795-0519-2 (In Rus).
16. Dzhonson N., Lion F. Statistika i planirovanie eksperimenta [Statistics and experiment planning]. M., Mir Publ., 1981. 520 p. (In Rus).
17. Hastie T. and Tibshirani R. Generalized additive models for medical research. Stat Methods Med Res, 1995. pp. 187-196.
18. Strode P. and Brokaw A. Using BioInteractive Resources to Teach. Mathematics and Statistics in Biology. Colorado, Rocky River High School, 2015, 42 р.
19. Novikov D.A., Novochadov V.V. Statisticheskie metodyi v mediko-biologicheskom eksperimente (tipovyie sluchai). [Statistical methods in the medical-biological experiment (typical cases)].Volgograd, 2005., 84 p. (In Rus).
20. Zaytsev V.M., Liflyandskiy V.G., Marinkin V.I. Prikladnaya meditsinskaya statistika, [Applied Medical Statistics] SPb., FOLIANT Publ., 2003., 432 p. (In Rus).
21. Glants S.. Mediko-biologicheskaya statistika. [Medical and Biological Statistics]. M., Praktika Publ, 1998. 459 p. (In Rus).
22. Yunkerov V.I., Grigorev S.G.. Matematiko-statisticheskaya obrabotka dannyih meditsinskih issledovaniy. [Mathematical and statistical processing of medical research data.] SPb., VMedA Publ., 2002., 266 p. (In Rus).
23. Merry R.J.E., Steinbuch M. and van de Molengraft M.J.G. Wavelet Theory and Applications a literature study. Eindhoven Univ. of Technology Dep. of Mechanical Engin. Control Systems Technol. Group, 2005, 41 p.
24. Lee Daniel T.L. and Yamamoto A.. Wavelet analysis theory and application. Hewlett-Packard Company. 1994, pp. 44-52