Journal article Open Access

Spirometry Data Analysis and Monitoring in Medical and Physiological Tests

Sokolov Oleksandr; Dobosz Krzysztof; Dreszer Joanna; Duch Włodzisław; Grzelak Sławomir; Komendziński Tomasz; Mikołajewski Dariusz; Piotrowski Tomasz; Świerkocka Małgorzata; Weber Piotr

Sokolov Oleksandr, Dobosz Krzysztof, Dreszer Joanna, Duch Włodzisław, Grzelak Sławomir, Komendziński Tomasz, Mikołajewski Dariusz, Piotrowski Tomasz, Świerkocka Małgorzata, Weber Piotr. Spirometry Data Analysis and Monitoring in Medical and Physiological Tests. Journal of Education, Health and Sport. 2015;5(3):35-46. ISSN 2391-8306. DOI: 10.5281/zenodo.16171

http://ojs.ukw.edu.pl/index.php/johs/article/view/2015%3B5%283%29%3A35-46

https://pbn.nauka.gov.pl/works/546923

http://dx.doi.org/10.5281/zenodo.16171

Formerly Journal of Health Sciences. ISSN 1429-9623 / 2300-665X. Archives 2011 – 2014 http://journal.rsw.edu.pl/index.php/JHS/issue/archive

 

Deklaracja.

Specyfika i zawartość merytoryczna czasopisma nie ulega zmianie.

Zgodnie z informacją MNiSW z dnia 2 czerwca 2014 r., że w roku 2014 nie będzie przeprowadzana ocena czasopism naukowych; czasopismo o zmienionym tytule otrzymuje tyle samo punktów co na wykazie czasopism naukowych z dnia 31 grudnia 2014 r.

The journal has had 5 points in Ministry of Science and Higher Education of Poland parametric evaluation. Part B item 1089. (31.12.2014).

© The Author (s) 2015;

This article is published with open access at Licensee Open Journal Systems of Kazimierz Wielki University in Bydgoszcz, Poland and Radom University in Radom, Poland

Open Access. This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium,

provided the original author(s) and source are credited. This is an open access article licensed under the terms of the Creative Commons Attribution Non Commercial License

(http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted, non commercial use, distribution and reproduction in any medium, provided the work is properly cited.

This is an open access article licensed under the terms of the Creative Commons Attribution Non Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted, non commercial

use, distribution and reproduction in any medium, provided the work is properly cited.

The authors declare that there is no conflict of interests regarding the publication of this paper.

Received: 20.01.2014. Revised 27.02.2015. Accepted: 12.03.2015.

 

Spirometry Data Analysis and Monitoring in Medical and Physiological Tests

 

Oleksandr Sokolov1, Krzysztof Dobosz1, Joanna Dreszer2, 4, Włodzisław Duch1, 4, Sławomir Grzelak3, Tomasz Komendziński2, 4, Dariusz Mikołajewski1, 4, 5, Tomasz Piotrowski1, 4, Małgorzata Świerkocka4, Piotr Weber1

 

1 Department of Informatics, Faculty of Physics, Astronomy and Informatics, Nicolaus Copernicus University, Torun, Poland

2 Faculty of Humanities, Nicolaus Copernicus University, Toruń, Poland

3 Faculty of Physics, Astronomy and Informatics, Nicolaus Copernicus University, Torun, Poland

4 Neurocognitive Laboratory, Centre for Modern Interdisciplinary Technologies, Nicolaus Copernicus University, Toruń, Poland

5 Institute of Mechanics and Applied Computer Science, Kazimierz Wielki University, Bydgoszcz, Poland

 

Corresponding author:

prof. Oleksandr Sokolov

Department of Informatics

Faculty of Physics, Astronomy and Informatics

Nicolaus Copernicus University

ul. Grudziadzka 5,

87-100 Torun, Poland

e-mail: osokolov@is.umk.pl

 

Abstract

 

Research on the computational breath analysis constitute important part of current challenges within the medical sciences, artificial intelligence, and biomedical engineering. Despite efforts of scientists and clinicians current results seem be not satisfying. Computational models of breath processes based e.g. on fuzzy logic may constitute another breakthrough in aforementioned area offering completing position to the current state of the art, both in the area of theoretical and experimental computational neuroscience, and clinical applications. Aim of the study was to find out whether is true if our new concept of intelligent breath analysis system can constitute another step toward better analysis and understanding of the aforementioned processes.

 

Keywords:     breath measure; computational breath analysis; breath disorders; computational models; artificial intelligence.

Sokolov Oleksandr, Dobosz Krzysztof, Dreszer Joanna, Duch Włodzisław, Grzelak Sławomir, Komendziński Tomasz, Mikołajewski Dariusz, Piotrowski Tomasz, Świerkocka Małgorzata, Weber Piotr. Spirometry Data Analysis and Monitoring in Medical and Physiological Tests. Journal of Education, Health and Sport. 2015;5(3):35-46. ISSN 2391-8306. DOI: 10.5281/zenodo.16171 http://ojs.ukw.edu.pl/index.php/johs/article/view/2015%3B5%283%29%3A35-46 https://pbn.nauka.gov.pl/works/546923 http://dx.doi.org/10.5281/zenodo.16171
Files (398.6 kB)
Name Size
2015_5_3_35-46_2578-6340.pdf
md5:6efe33d5a3c3669811154dce3b0d36b4
398.6 kB Download
  • [10] Sensor Technical Documentation, Sensirion company, www.sensirion.com – access 18.11.2014.

  • [11] Valenza G, Lanat´a A, Scilingo EP. Improving emotion recognition systems by embedding cardiorespiratory coupling. Physiol. Meas. 2013; 34:449–464.

  • [12] Beauchamp J, Kirsch F, Buettner A. Real-time breath gas analysis for pharmacokinetics: monitoring exhaled breath by on-line proton-transfer-reaction mass spectrometry after ingestion of eucalyptol-containing capsules. J. Breath Res. 2010;4:026006.

  • [13] Kohl I, Beauchamp J, Cakar-Beck F et al. First observation of a potential non-invasive breath gas biomarker for kidney function J. Breath Res. 2013;7:017110.

  • [14] Thekedar B, Szymczak W, Hllriegl V, Hoeschen C, Oeh U. Investigations on the variability of breath gas sampling using PTR-MS. J. Breath Res. 2009;3:027007.

  • [14] Toyooka T, Hiyama S, Hamada Y. A prototype portable breath acetone analyzer for monitoring fat loss. J. Breath Res. 2013; 7:36005.

  • [15] Gólczewski T., Zieliński K. Parowski. Ferrari G. F. Pałko K J Parowski M Influence of ventilation mode on blood oxygenation - Investigation with Polish virtual lungs and Italian model of circulation. Biocybernetics and Biomedical Engineering 2010; 30(1):17-30.

  • [16] ICNT web site: http://www.icnt.umk.pl – access 18.11.2014.

  • [17] InteRDoCTor web site: http://www.interdoctor.umk.pl/index.html – access 18.11.2014.

  • [1] Mazur R, Świerkocka-Miastkowska M, Książkiewicz B, Princ R, Ogonowski A, Wołoszyn M. Spirografia mózgowa we wczesnym okresie udaru niedokrwiennego mózgu — doniesienie wstępne. Udar Mózgu 2002;4:1–6.

  • [2] Markram H. Seven challenges for neuroscience. Funct Neurol. 2013;28:145-151.

  • [3] Takens F. Detecting strange attractors in turbulence. In: Rand DA, Young L-S. Dynamical Systems and Turbulence, Lecture Notes in Mathematics, vol. 898. Heidelberg-New York: Springer-Verlag 1981, pp. 366–381.

  • [4] Butera R, Rinzel J, Smith J. Models of respiratory rhythm generation in the pre-Bötzinger complex: I. Bursting pacemaker neurons; II. Populations of coupled pacemakers. J Neurophysiol. 1999; 82:382–397,398–415.

  • [5] Osiński G, Świerkocka-Miastkowska M, Dobosz K. Numerical Simulations of Respiratory Rhythms and Brain Spirography in Coma. In: Coma and Consciousness. Clinical, Societal and Ethical Implications. Satellite Symposium of the 13th Annual Meeting of the Association for the Scientific Studies of Consciousness, Berlin, Germany, 2009.

  • [6] Dobosz K, Duch W. Understanding Neurodynamical Systems via FuzzySymbolic Dynamics. Neural Networks 2010; 23:487–496.

  • [7] Kakar M, Nystrom N, Aarup LR, Nøttrup TJ, Olsen DR. Respiratory motion prediction by using the adaptive neuro fuzzy inference system (ANFIS). Phys. Med. Biol. 2005; 50: 4721–4728.

  • [8] Homma I, Masaoka Y. Breathing rhythms and emotions. Exp Physiol. 2008;93:1011–1021.

  • [9] Marchewka A. Zurawski Ł, Jednoróg K, Grabowska A. The Nencki Affective Picture System (NAPS): introduction to a novel, standardized, wide-range, high-quality, realistic picture database. Behav Res. 2014;46:596-610.

57
12
views
downloads
All versions This version
Views 5757
Downloads 1212
Data volume 4.8 MB4.8 MB
Unique views 5656
Unique downloads 1212

Share

Cite as