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Variability analysis of the respiratory volume based on non-linear prediction methods

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Abstract

This work proposed and studied a method of automatically classifying respiratory volume signals as high or low variability by means of non-linear analysis of the respiratory volume. The analysis used volume signals generated by the respiratory system to construct a model of its dynamics and to estimate the quality of the predictions made with the model. Different methods of prediction evaluation, prediction horizons and embedding dimensions were also analysed. Assessment of the method was made using a database that contained 40 respiratory volume signals classified using clinical criteria into two classes: low or high variability. The results obtained using the method of surrogate data provided evidence of non-linear determinism in the respiratory volume signals. A discriminant analysis carried out using non-linear prediction variables classified the respiratory volume signals with an accuracy of 95%.

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References

  • Abarbanel, H. D., Brown, R., andKadtke, J. B. (1989): ‘Prediction and chaotic nonlinear systems: Methods for time series with broad-band Fourier spectra’,Phys. Rev. A,41, pp. 1782–1807

    MathSciNet  Google Scholar 

  • Achermann, P., Hartmann, R., Gunziger, A., Guggenbuhl, W., andBrobely, A. A. (1994): ‘All-night sleep EEG and artificial stochastic control signals have similar correlation dimensions’,Electroencephalogr. Clin. Neurophisiol.,90, pp. 384–387

    Google Scholar 

  • Akay, M., Lipping, T., Moodie, K., andHoopes, P. J. (2002): ‘Effects of hypoxia on the complexity of respiratory patterns during maturation’,Early Hum. Devel.,70, pp. 55–71

    Google Scholar 

  • Brack, T., Jubran, A., andTobin, M. J. (2002): ‘Dyspnea and decreased variability of breathing in patients with restrictive lung disease’,Am. J. Respir. Crit. Care Med.,165, pp. 1260–1264

    Article  Google Scholar 

  • Bruce, E. N., andDaubenspeck, J. A. (1995): ‘Mechanisms and analysis of respiratory variability’, in ‘Control of breathing’, (Marcel Dekker, 1995), pp. 285–314

  • Bruce, E. N. (1996a): ‘Temporal variations in the pattern of breathing’,J. Appl. Physiol.,80, pp. 1079–1087

    Google Scholar 

  • Bruce, E. N. (1996b): ‘Measures of respiratory pattern variability’, in ‘Bioengineering approaches to pulmonary physiology and medicine’ (Plenum Press, 1996), pp. 149–160

  • Capdevila, X., Perrigault, P. F., Ramonatxo, M., Roustan, J. P., Peray, P., Francoise, A., andPrefaut, C. (1998): ‘Changes in breathing pattern and respiratory muscle performance parameters during difficult weaning’,Crit. Care Med.,26, pp. 79–87

    Google Scholar 

  • Censi, F., Calcagnini G., Lino, S., Seydnejad, S. R., Kitney, R. I., andCerutti, S. (2000): ‘Transient phase locking patterns among respiration, heart rate and blood pressure during cardiorespiratory synchronisation in humans’,Med. Biol. Eng. Comput.,38, pp. 416–426

    Article  Google Scholar 

  • Del Rosario, N., Sassoon, C. S., Chetty, K. G., Gruer, S. E., andMahutte, C. K. (1997): ‘Breathing pattern during acute respiratory failure and recovery’,Eur. Respir. J.,10, pp. 2560–2565

    Google Scholar 

  • Hoyer, D., Kaplan, D. T., Shaaf, F., andEiselt, M. (1998): ‘Determinism in bivariate cardiorespiratory phase space sets. How to detect nonlinear coordinations’,IEEE Eng. Med. Biol.,17, pp. 26–31

    Google Scholar 

  • Hoyer, D., Leder, U., Hoyer, H., Pompe, B., Sommer, M., andZwiener, U. (2002): ‘Mutual information and phase dependencies: Measures of reduced non-linear cardio-respiratory interactions after myocardial infarction’,Med. Eng. Phys.,24, pp. 33–43

    Article  Google Scholar 

  • Jubran, A., Grant, B. J. B., andTobin, M. J. (1997): ‘Effect of hyperoxic hypercapnia on variational activity of breathing’,Am. J. Respir. Crit. Care Med.,156, pp. 1129–1139

    Google Scholar 

  • Kantz, H. andSchreiber, T. (2000) ‘Determinism and predictability’, in ‘Nonlinear time series analysis’, (Cambridge University Press, 2000), pp. 42–57

  • Kaplan, D., andGlass, L. (1995): ‘Characterizing chaos’, in ‘Understanding nonlinear dynamics’, (Springer-Verlag, 1995) pp. 314–338

  • Khoo, M. C. K. (2000): ‘Determinants of ventilatory instability and variability’,Respir. Physiol.,122, pp. 167–182

    Article  Google Scholar 

  • Modarreszadeh, M., Bruce, E. N., andGothe, B. (1990): ‘Non-random variability in respiratory cycle parameters of humans during stage 2 sleep’,J. Appl. Physiol.,69, pp. 630–639

    Google Scholar 

  • Rigney, D. R., Ocasio, W. C., Clark, K. P., Wei, J. Y., andGoldberger, A. L. (1992): ‘Deterministic mechanism for chaos and oscillations in heart rate and blood pressure’,Circulation, pp. 651–659

  • Sammon, M., Romaniuk, J. R., andBruce, E. (1993): ‘Bifurcations of the respiratory pattern associated with reduced lung volume in the rat’,J. Appl. Physiol.,75, pp. 887–901

    Google Scholar 

  • Schreiber, T., andSchmitz, A. (1996): ‘Improved surrogate data for nonlinearity tests’,Phys. Rev. Lett.,77, pp. 635–638

    Article  Google Scholar 

  • Schreiber, T., andSchmitz, A. (1997): ‘Discrimination power of measures for nonlinearity in a time series’,Phys. Rev. E,55, pp. 5443–5447

    Article  Google Scholar 

  • Small, M., Judd, K., Lowe, M., andStick, S. (1999): ‘Is breathing in infants chaotic? Dimension estimates for respiratory patterns during quiet sleep’,J. Appl. Physiol.,86, pp. 359–376

    Google Scholar 

  • Tapanainen, J. M., Seppänen, M. D., Laukkanen, R., Loimaala, A., andHuikuri, H. V. (1999): ‘Significance of the accuracy of RR interval detection for the analysis of new dynamic measures of heart rate variability’,Ann. Noninvas. Electrocardiol.,4, pp. 10–18

    Google Scholar 

  • Theiler, J., Eubank, S. E., Longtin, A., Galdrikian, B., andFarmer, D. (1992): ‘Testing for nonlinearity in time series: the method of surrogate data’,Phys. D,58, pp. 77–94

    Article  Google Scholar 

  • Tobin, M. J. (2001): ‘Advances in mechanical ventilation’,New Engl. J. Med.,344, pp. 1986–1996

    Article  Google Scholar 

  • Turcott, R. G., andTeich, M. C. (1996): ‘Fractal character of the electrocardiogram: distinguishing heart failure and normal patients’,Ann. Biomed. Eng.,24, pp. 269–293

    Google Scholar 

  • Van Den Aardweg, J. G., andKaremaker, J. M. (2002): ‘Influences of chemoreflexes on respiratory variability in healthy subjects’,Am. J. Respir. Crit. Care Med.,165, pp. 1041–1047

    Google Scholar 

  • Wessel, N., Meyerfeldt, U., Schirdewan, A., Kurths, J., andVoss, A. (1998): ‘Short-term forecasting of life-threatening arrhythmias with finite time Lyapunov exponents’,Ann. Conf. IEEE Eng. Med. Biol. Soc.,20, pp. 326–329

    Google Scholar 

  • Wrigge, H., Golisch, W., Zinserling, J., Sydow, M., Almeling, G., andBurchardi, H. (1999): ‘Proportional assist versus pressure support ventilation: effects on breathing pattern and respiratory work of patients with chronic obstructive pulmonary disease’,Intens. Care Med.,25, pp. 790–798

    Google Scholar 

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Correspondence to P. Caminal.

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Caminal, P., Dominge, L., Giraldo, B.F. et al. Variability analysis of the respiratory volume based on non-linear prediction methods. Med. Biol. Eng. Comput. 42, 86–91 (2004). https://doi.org/10.1007/BF02351015

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  • DOI: https://doi.org/10.1007/BF02351015

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