A novel approach towards non-obstructive detection and classification of COPD using ECG derived respiration

  • Surita Sarkar
  • Parthasarathi Bhattacharyya
  • Madhuchhanda Mitra
  • Saurabh PalEmail author
Scientific Paper


The alarming rate of mortality and disability due to Chronic Obstructive Pulmonary Disease (COPD) has become a serious health concern worldwide. The progressive nature of this disease makes it inevitable to detect this disease in its early stages, leads to a greater demand for developing non-obstructive and reliable technology for COPD detection. The use of highly patient-effort dependent, time-consuming, and expensive methods are some major inherent limitations of previous techniques. Lack of knowledge about the disease and inadequacy of proper diagnostic tool for early detection of COPD is another reason behind the 3rd leading cause of death worldwide. For this reason, this study aims to explore the utility of ECG Derived Respiration (EDR) for classification between COPD patients and normal healthy subjects as EDR can be easily extracted from ECG. ECG and respiration signals collected from 30 normal and 30 COPD subjects were analysed. Error calculation and statistical analysis were performed to observe the similarity between original respiration and EDR signal. The morphological pattern changes of respiration and EDR signals were analysed and three different features were extracted from those. Classification was performed by different classifiers employing Decision Tree, Linear Discriminant Analysis (LDA), Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). Apart from obtaining comparable classification performance it was seen that EDR has better potential than the original respiration signal for classification of COPD from normal population.


Chronic Obstructive Pulmonary Disease Electrocardiogram ECG derived respiration Respiration Classification 



The authors would like to thank the medical technicians and research assistants of Institute of Pulmocare & Research, Kolkata, India for their valuable assistance in this study. The first author acknowledges the support of Council of Scientific & Industrial Research, Human Resource Development Group, India through the CSIR-SRF Fellowship.

Compliance with ethical standards

Conflict of interest

The authors declare that there are no conflicts of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.


  1. 1.
    WHO (2017) Fact sheet. The top ten causes of death. Fact Sheet-310. World Health Organization, Geneva.Google Scholar
  2. 2.
    Vrbica Z, Labor M, Gudelj I, Labor S, Juric I, Plavec D (2017) Early detection of COPD patients in GOLD 0 population: an observational non-interventional cohort study- MARKO study. BMC Pulm Med 17(1):36–923CrossRefGoogle Scholar
  3. 3.
    van Schayck OCP, D’Urzo A, Invernizzi G, Roman M, Stallberg B, UrbinaC, (2003) Early detection of chronic obstructive pulmonary disease (COPD): the role of spirometry as a diagnostic tool in primary care. Prim Care Respir J 12(3):90–93CrossRefGoogle Scholar
  4. 4.
    Decramer M, Miravitlles M, Price D, Rodriguez MR, Llor C, Welte T, Buhl R, Dusser D, Samara K, Siafakas N (2011) New horizons in early stage COPD-improving knowledge, detection and treatment. Respir Med 105:1576–1587–1587CrossRefGoogle Scholar
  5. 5.
    Rabe KF (2007) Global initiative for chronic obstructive lung disease (GOLD). Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease. Am J Respir Crit Care Med 176:532–555CrossRefGoogle Scholar
  6. 6.
    Petty TL (2005) Benefits of and barriers to the widespread use of spirometry. Curr Opin Pulm Med 11:115–120PubMedGoogle Scholar
  7. 7.
    Yamauchi Y, Kohyama T, Jo T, Nagase T (2012) Dynamic change in respiratory resistance during inspiratory and expiratory phases of tidal breathing in patients with chronic obstructive pulmonary disease. Int J Chronic Obstr Pulm Dis 7:259–269CrossRefGoogle Scholar
  8. 8.
    Taplin GV, Tashkin DP, Chopra SK, Anselmi OE, Elam D, Calvarese B, Coulson A, Detels R, Rokav SN (1977) Early detection of chronic obstructive pulmonary disease using radionuclide lung-imaging procedures. Chest 71:567–575CrossRefGoogle Scholar
  9. 9.
    Hosseini MP, Zadeh HS, Akhlaghpoor S (2012) Detection and severity scoring of chronic obstructive pulmonary disease using volumetric analysis of lung CT images. Iran J Radiol 9(1):22–27CrossRefGoogle Scholar
  10. 10.
    Van Berkel JJBN, Dallinga JW, Moller GM, Godschalk RWL, Moonen EJ, Wouters EFM, van Schooten FJ (2010) A profile of volatile organic compounds in breath discriminates COPD patients from controls. Respir Med 104:557–563CrossRefGoogle Scholar
  11. 11.
    Dellaca RL, SantusP Aliverti A, Stevenson N, Centanni S, Macklem PT, Pedotti A, Calverley PMA (2004) Detection of expiratory flow limitation in COPD using the forced oscillation technique. Eur Respir J 23:232–240CrossRefGoogle Scholar
  12. 12.
    Frantz S, Nihlen U, Dencker M, Engstrom G, Lofdahl CG, Wollmer P (2012) Impulse oscillometry may be of value in detecting early manifestations of COPD. Respir Med 106:1116–1123CrossRefGoogle Scholar
  13. 13.
    Mieloszyk RJ, Verghese GC, Deitch K, Cooney B, Khalid A, Mirre-González MA, Heldt T, Krauss BS (2014) Automated quantitative analysis of capnogram shape for COPD-normal and COPD-CHF classification. IEEE Trans Biomed Eng 61(12):2882–2890CrossRefGoogle Scholar
  14. 14.
    Velasquez A, Duran CM, Gualdron O, Rodriguez JC, Manjarres L (2009) Electronic nose to detect patients with COPD from exhaled breath. AIP Conf Proc 1137(1):452CrossRefGoogle Scholar
  15. 15.
    Warnier MJ, Rutten FH, Numans ME, Kors JA, Tan HL, de Boer A, Hoes AW, De Bruin M (2013) Electrocardiographic characteristics of patients with chronic obstructive pulmonary disease COPD. J Chronic Obstr Pulm Dis 10:62–71CrossRefGoogle Scholar
  16. 16.
    Rachaiah NM, Rachaiah JM, Krishnaswamy RB (2012) A correlative study of spirometric and ECG changes in patients with chronic obstructive pulmonary disease. Int J Biol Med Res 3(1):1322–1326Google Scholar
  17. 17.
    Loveridge B, West P, Anthonisen NR, Kryger MH (1984) Breathing patterns in patients with chronic obstructive pulmonary disease. Am Rev Respir Dis 130:730–733PubMedGoogle Scholar
  18. 18.
    Moody GB, Mark RG, Zoccola A, Mantero S (1985) Derivation of respiratory signals from multi-lead ECGs. Comput Cardiol 12(1985):113–116Google Scholar
  19. 19.
    Zhao L, Reisman S, Findley T (1994) Derivation of respiration from electrocardiogram during heart rate variability studies. Computers in Cardiology, Bethesda, Maryland, USAGoogle Scholar
  20. 20.
    Lado MJ, Méndez AJ, Vila XA, Rodríguez-Liñares L, Félix P (2012) HRV patterns and exacerbations of COPD patients following routine controls: a preliminary study. 7th Iberian Conference on Information Systems and Technologies, Madrid.Google Scholar
  21. 21.
    Pantoni CBF, Reis MS, Martins LEB, Catai AM, Costa D, Borghi-Silva A (2007) Study of heart rate autonomic modulation at rest in elderly patients with chronic obstructive pulmonary disease. RevistaBrasileira de Fisioterapia 11(1):33–38Google Scholar
  22. 22.
    Handa R, Paonta L, Rusu D, Albu A (2012) The role of heart rate variability in assessing the evolution of patients with chronic obstructive pulmonary disease. Rom J Intern Med 50:83–88PubMedGoogle Scholar
  23. 23.
    Celli BR, MacNee W (2004) Standards for the diagnosis and treatment of patients with COPD: asummary of the ATS/ERS position paper. Eur Respir J 23(6):932–946CrossRefGoogle Scholar
  24. 24.
    Pflanzer R, McMullen W (2014) Electrocardiography (ECG) I Introduction Biopac Student Lab Manual. BIOPAC Systems Inc, GoletaGoogle Scholar
  25. 25.
    Sobron A, Romero I, Lopetegi T (2010) Evaluation of methods of respiratory frequency from the ECG. Computing in Cardiology, IEEE, New JerseyGoogle Scholar
  26. 26.
    Chan AM, Ferdosi N, Narasimhan R (2013) Ambulatory respiratory rate detector using ECG and a triaxial accelerometer. 35th Annual International Conference of the IEEE EMBS, Osaka.Google Scholar
  27. 27.
    Sarkar S, Bhattacherjee S, Pal S (2015) Extraction of respiration signal from ECG for respiratory rate estimation. Proceeding of Michael Faraday IET International Summit, KolkataCrossRefGoogle Scholar
  28. 28.
    Sarkar S, Bhattacherjee S, Bhattachrayya P, Pal S (2016) Differentiation of COPD from normal population using ECG derived respiration: a pilot observation. Pulmo-Face XV I(1):12–18Google Scholar
  29. 29.
    Rodgers JL, Nicewander WA (1988) Thirteen ways to look at the correlation coefficient. Am Stat 42(1):59–66CrossRefGoogle Scholar
  30. 30.
    Hastie T, Tibshirani R, Friedman J (2009) Elements of statistical learning, 2nd edn. Springer, New YorkCrossRefGoogle Scholar
  31. 31.
    Christianini N, Taylor JS (2000) An introduction to support vector machines and other kernel-based learning methods, 1st edn. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  32. 32.
    Sahin D, Ubeyli ED, Ilbay G, Sahin M, Yasar AB (2010) Diagnosis of airway obstruction or restrictive spirometric patterns by multiclass support vector machines. J Med Syst 34:967–973CrossRefGoogle Scholar
  33. 33.
    Vapnik VN (1999) The nature of statistical learning theory, 2nd edn. Springer, New YorkGoogle Scholar
  34. 34.
    Parvin H, Alizadeh H, Minati B (2010) A modification on K-nearest neighbor classifier. Glob J Comput Sci Technol 10(14):37–41Google Scholar
  35. 35.
    Piotrowski Z, Szypulska M (2017) Classification of falling asleep states using HRV analysis. Biocybern Biomed Eng 37:290–301CrossRefGoogle Scholar
  36. 36.
    Kim KS, Choi HH, Moon CS, Mun CW (2011) Comparison of k-nearest neighbor, quadratic discriminant and linear discriminant analysis in classification of electromyogram signals based on the wrist-motion directions. Curr Appl Phys 11:740–745CrossRefGoogle Scholar
  37. 37.
    Reardon M, Malik M (1996) Changes in heart rate variability with age. Pace 19:1863–1866CrossRefGoogle Scholar
  38. 38.
    Cysarz D, Zerm R, Bettermann H, Fruhwirth M, Moser M, Kroz M (2008) Comparison of respiratory rates derived from heart rate variability, ECG amplitude, and nasal/oral airflow. Ann Biomed Eng 36(12):2085–2094CrossRefGoogle Scholar
  39. 39.
    van Ravenswaaij CMA, Kollee LAA, Hopman JCW, Stoelinga GBA, van Geijn HP (1993) Heart rate variability. Ann Intern Med 118:436–447CrossRefGoogle Scholar
  40. 40.
    Phillips C, Parthaláin NM, Syed Y, Deganello D, Claypole T, Lewis K (2014) Short-term intra-subject variation in exhaled volatile organic compounds (VOCs) in COPD patients and healthy controls and its effect on disease classification. Metabolites 4:300–318CrossRefGoogle Scholar
  41. 41.
    Patel S, Mancinelli C, Bonato P, Healey J, Moy M (2009) Using wearable sensors to monitor physical activities of patients with COPD: a comparison of classifier performance. Body Sens Netw 234–239.Google Scholar
  42. 42.
    Caliskan SG, Bilgin MD, Polatli M (2018) Nonlinear analysis of electrodermal activity signals for healthy subjects and patients with chronic obstructive pulmonary disease. Australas Phys Eng Sci Med 41:487–494CrossRefGoogle Scholar
  43. 43.
    Malmberg LP, Pesu L, Sovijarvi ARA (1995) Significant differences in flow standardised breath sound spectra in patients with chronic obstructive pulmonary disease, stable asthma, and healthy lungs. Thorax 50:1285–1291CrossRefGoogle Scholar
  44. 44.
    Fard PJM, Moradi MH, Saber S (2015) Chaos to randomness: distinguishing between healthy and non-healthy lung sound behaviour. Australas Phys Eng Sci Med 38:47–54CrossRefGoogle Scholar
  45. 45.
    de Chazel P, Heneghan C, Sheridan E, Reilly R, Nolan P, O’Malley M (2003) Automated processing of the single-lead electrocardiogram for the detection of obstructive sleep apnoea. IEEE Trans Biomed Eng 50(6):686–696CrossRefGoogle Scholar
  46. 46.
    Pichot V, Chouchou F, Pepin JL, Tamisier R, Levy P, Fortune IC, Sfroza E, Barthelemy JC, Roche F (2015) ECG-derived respiration: a promising tool for sleep-disordered breathing diagnosis in chronic heart failure patients. Int J Cardiol 186:7–9CrossRefGoogle Scholar
  47. 47.
    Volterrani M, Scalvini S, Mazzuero G, Lanfranchi P, Colombo R, Clark AL, Levi G (1994) Decreased heart rate variability in patients with chronic obstructive pulmonary disease. Chest 106(5):1432–1437CrossRefGoogle Scholar
  48. 48.
    Skyba P, Joppa P, Orolín M, Tkáčová R (2007) Blood pressure and heart rate variability response to noninvasive ventilation in patients with exacerbations of chronic obstructive pulmonary disease. Physiol Res 56:527–533PubMedGoogle Scholar
  49. 49.
    Bartels MN, Jelic S, Ngai P, Banser RC, DeMeersman RE (2003) High-frequency modulation of heart rate variability during exercise in patients with COPD. Chest 124:863–869CrossRefGoogle Scholar
  50. 50.
    Sarkar S, Pal S, Bhattacharyya P (2019) A comparative analysis between EDR and respiration signal: a pilot study with normal subjects. Modelling and simulation in science, technology and engineering mathematics, advances in intelligent systems and computing. Springer, Cham.Google Scholar
  51. 51.
    Parreira VF, Bueno CJ, França DC, Vieira DS, Pereira DR, Britto RR (2010) Breathing pattern and thoracoabdominal motion in healthy individuals: influence of age and sex. Braz J Phys Ther 14(5):411–416CrossRefGoogle Scholar
  52. 52.
    Gabriel L, Hoffman M, Mendes L, Samora G, Rattes C, Dornelas A, Britto R, Parreira V (2015) Comparison of breathing pattern and thoracoabdominal motion of healthy elderly. Eur Respir Soc 46:PA4217Google Scholar
  53. 53.
    Mendes LPDS, Vieira DSR, Gabriel LS, Ribeiro-Samora GA, De Andrade AD, Brandão DC, Goes MC, Fregonezi GAF, Britto RR, Parreira VF (2019) Influence of posture, sex, and age on breathing pattern and chest wall motion in healthy subjects. Braz J Phys Ther. CrossRefPubMedGoogle Scholar

Copyright information

© Australasian College of Physical Scientists and Engineers in Medicine 2019

Authors and Affiliations

  1. 1.Department of Applied PhysicsUniversity of CalcuttaKolkataIndia
  2. 2.Institute of Pulmocare & ResearchKolkataIndia

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