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Journal of Medical and Biological Engineering

, Volume 37, Issue 6, pp 791–799 | Cite as

Detrended Fluctuation Analysis of Oxyhemoglobin Saturation by Pulse Oximetry in Sleep Apnea Syndrome

  • Chung-Ching Hua
  • Chung-Chieh Yu
Original Article

Abstract

Variability of oxyhemoglobin saturation during sleep has been utilized as diagnostic index for sleep apnea. This work examined the parameters of a detrended fluctuation analysis (DFA) plot created from series data for oxyhemoglobin saturation during a sleep study in 273 subjects. A novel automated algorithm was devised to measure the parameters of a DFA log–log plot that included the slopes of 4 line segments and the coordinates and angles of their intersections. The diagnostic value of the parameters was investigated by receiver operating characteristic (ROC) curves using apnea/hypopnea index (AHI) cutoff values of 5, 15, and 30. Three of the DFA plot parameters exhibited an area under the ROC curve ≥0.89 for all three AHI cutoff values. Principal component analysis found a surrogate variable that increased the areas under ROC curves to greater than 0.92 for all of the AHI cutoff values. The algorithm was “leave-one-out” cross-validated and validated in another 206 subjects receiving polysomnographic studies. The results demonstrate that the DFA plot of oxyhemoglobin saturation is a useful tool for screening subjects with sleep apnea.

Keywords

Detrended fluctuation analysis Obstructive sleep apnea Oxyhemoglobin saturation 

JEL code

C22 

References

  1. 1.
    Caples, S. M., Gami, A. S., & Somers, V. K. (2005). Obstructive sleep apnea. Annals of Internal Medicine, 142(3), 187–197.CrossRefGoogle Scholar
  2. 2.
    Flemons, W. W., Littner, M. R., Rowley, J. A., Gay, P., Anderson, W. M., Hudgel, D. W., et al. (2003). Home diagnosis of sleep apnea: A systematic review of the literature. An evidence review cosponsored by the American Academy of Sleep Medicine, the American College of Chest Physicians, and the American Thoracic Society. Chest, 124(4), 1543–1579.CrossRefGoogle Scholar
  3. 3.
    Magalang, U. J., Dmochowski, J., Veeramachaneni, S., Draw, A., Mador, M. J., El-Solh, A., et al. (2003). Prediction of the apnea-hypopnea index from overnight pulse oximetry. Chest, 124(5), 1694–1701.CrossRefGoogle Scholar
  4. 4.
    Bennett, J. A., & Kinnear, W. J. (1999). Sleep on the cheap: the role of overnight oximetry in the diagnosis of sleep apnoea hypopnoea syndrome. Thorax, 54(11), 958–959.CrossRefGoogle Scholar
  5. 5.
    Marcos, J. V., Hornero, R., Alvarez, D., Del Campo, F., & Aboy, M. (2010). Automated detection of obstructive sleep apnoea syndrome from oxygen saturation recordings using linear discriminant analysis. Medical and Biological Engineering and Computing, 48(9), 895–902.CrossRefGoogle Scholar
  6. 6.
    Marcos, J. V., Hornero, R., Alvarez, D., del Campo, F., Lopez, M., & Zamarron, C. (2008). Radial basis function classifiers to help in the diagnosis of the obstructive sleep apnoea syndrome from nocturnal oximetry. Medical and Biological Engineering and Computing, 46(4), 323–332.CrossRefGoogle Scholar
  7. 7.
    Marcos, J. V., Hornero, R., Alvarez, D., del Campo, F., & Zamarron, C. (2009). Assessment of four statistical pattern recognition techniques to assist in obstructive sleep apnoea diagnosis from nocturnal oximetry. Medical Engineering & Physics, 31(8), 971–978.CrossRefGoogle Scholar
  8. 8.
    Marcos, J. V., Hornero, R., Alvarez, D., Del Campo, F., Zamarron, C., & Lopez, M. (2008). Utility of multilayer perceptron neural network classifiers in the diagnosis of the obstructive sleep apnoea syndrome from nocturnal oximetry. Computer Methods and Programs in Biomedicine, 92(1), 79–89.CrossRefGoogle Scholar
  9. 9.
    Marcos, J. V., Hornero, R., Alvarez, D., Nabney, I. T., Del Campo, F., & Zamarron, C. (2010). The classification of oximetry signals using Bayesian neural networks to assist in the detection of obstructive sleep apnoea syndrome. Physiological Measurement, 31(3), 375–394.CrossRefGoogle Scholar
  10. 10.
    Morillo, D. S., Rojas, J. L., Crespo, L. F., Leon, A., & Gross, N. (2009). Poincare analysis of an overnight arterial oxygen saturation signal applied to the diagnosis of sleep apnea hypopnea syndrome. Physiological Measurement, 30(4), 405–420.CrossRefGoogle Scholar
  11. 11.
    Zamarron, C., Gude, F., Barcala, J., Rodriguez, J. R., & Romero, P. V. (2003). Utility of oxygen saturation and heart rate spectral analysis obtained from pulse oximetric recordings in the diagnosis of sleep apnea syndrome. Chest, 123(5), 1567–1576.CrossRefGoogle Scholar
  12. 12.
    Alvarez, D., Hornero, R., Abasolo, D., del Campo, F., Zamarron, C., & Lopez, M. (2009). Nonlinear measure of synchrony between blood oxygen saturation and heart rate from nocturnal pulse oximetry in obstructive sleep apnoea syndrome. Physiological Measurement, 30(9), 967–982.CrossRefGoogle Scholar
  13. 13.
    de Chazal, P., Heneghan, C., & McNicholas, W. T. (2009). Multimodal detection of sleep apnoea using electrocardiogram and oximetry signals. Philosophical Transactions of the Royal Society of London A, 367(1887), 369–389.CrossRefGoogle Scholar
  14. 14.
    Heneghan, C., Chua, C. P., Garvey, J. F., de Chazal, P., Shouldice, R., Boyle, P., et al. (2008). A portable automated assessment tool for sleep apnea using a combined Holter-oximeter. Sleep, 31(10), 1432–1439.Google Scholar
  15. 15.
    Narkiewicz, K., van de Borne, P. J., Pesek, C. A., Dyken, M. E., Montano, N., & Somers, V. K. (1999). Selective potentiation of peripheral chemoreflex sensitivity in obstructive sleep apnea. Circulation, 99(9), 1183–1189.CrossRefGoogle Scholar
  16. 16.
    Suki, B. (2002). Fluctuations and power laws in pulmonary physiology. American Journal of Respiratory and Critical Care Medicine, 166(2), 133–137.CrossRefGoogle Scholar
  17. 17.
    Seely, A. J., & Macklem, P. T. (2004). Complex systems and the technology of variability analysis. Critical Care, 8(6), R367–R384.CrossRefGoogle Scholar
  18. 18.
    Makikallio, T. H., Koistinen, J., Jordaens, L., Tulppo, M. P., Wood, N., Golosarsky, B., et al. (1999). Heart rate dynamics before spontaneous onset of ventricular fibrillation in patients with healed myocardial infarcts. The American Journal of Cardiology, 83(6), 880–884.CrossRefGoogle Scholar
  19. 19.
    Tapanainen, J. M., Thomsen, P. E., Kober, L., Torp-Pedersen, C., Makikallio, T. H., Still, A. M., et al. (2002). Fractal analysis of heart rate variability and mortality after an acute myocardial infarction. The American Journal of Cardiology, 90(4), 347–352.CrossRefGoogle Scholar
  20. 20.
    Laitio, T. T., Huikuri, H. V., Kentala, E. S., Makikallio, T. H., Jalonen, J. R., Helenius, H., et al. (2000). Correlation properties and complexity of perioperative RR-interval dynamics in coronary artery bypass surgery patients. Anesthesiology, 93(1), 69–80.CrossRefGoogle Scholar
  21. 21.
    Laitio, T. T., Huikuri, H. V., Makikallio, T. H., Jalonen, J., Kentala, E. S., Helenius, H., et al. (2004). The breakdown of fractal heart rate dynamics predicts prolonged postoperative myocardial ischemia. Anesthesia and Analgesia, 98(5), 1239–1244.CrossRefGoogle Scholar
  22. 22.
    Ho, K. K., Moody, G. B., Peng, C. K., Mietus, J. E., Larson, M. G., Levy, D., et al. (1997). Predicting survival in heart failure case and control subjects by use of fully automated methods for deriving nonlinear and conventional indices of heart rate dynamics. Circulation, 96(3), 842–848.CrossRefGoogle Scholar
  23. 23.
    Varela, M., Churruca, J., Gonzalez, A., Martin, A., Ode, J., & Galdos, P. (2006). Temperature curve complexity predicts survival in critically ill patients. American Journal of Respiratory and Critical Care Medicine, 174(3), 290–298.CrossRefGoogle Scholar
  24. 24.
    Hwa, R. C., & Ferree, T. C. (2002). Scaling properties of fluctuations in the human electroencephalogram. Physical Review E: Statistical, Nonlinear, and Soft Matter Physics, 66(2 Pt 1), 021901.CrossRefGoogle Scholar
  25. 25.
    Stam, C. J., Montez, T., Jones, B. F., Rombouts, S. A., van der Made, Y., Pijnenburg, Y. A., et al. (2005). Disturbed fluctuations of resting state EEG synchronization in Alzheimer’s disease. Clinical Neurophysiology, 116(3), 708–715.CrossRefGoogle Scholar
  26. 26.
    Goldberger, A. L., Amaral, L. A., Hausdorff, J. M., Ivanov, P., Peng, C. K., & Stanley, H. E. (2002). Fractal dynamics in physiology: alterations with disease and aging. Proceedings of National Academic Science U S A, 99(Suppl 1), 2466–2472.CrossRefGoogle Scholar
  27. 27.
    Peng, C. K., Havlin, S., Stanley, H. E., & Goldberger, A. L. (1995). Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos, 5(1), 82–87.CrossRefGoogle Scholar
  28. 28.
    Makikallio, T. H., Ristimae, T., Airaksinen, K. E., Peng, C. K., Goldberger, A. L., & Huikuri, H. V. (1998). Heart rate dynamics in patients with stable angina pectoris and utility of fractal and complexity measures. The American Journal of Cardiology, 81(1), 27–31.CrossRefGoogle Scholar
  29. 29.
    Huikuri, H. V., Makikallio, T. H., Peng, C. K., Goldberger, A. L., Hintze, U., & Moller, M. (2000). Fractal correlation properties of R–R interval dynamics and mortality in patients with depressed left ventricular function after an acute myocardial infarction. Circulation, 101(1), 47–53.CrossRefGoogle Scholar
  30. 30.
    Chen, Z., Ivanov, P., Hu, K., & Stanley, H. E. (2002). Effect of nonstationarities on detrended fluctuation analysis. Physical Review E: Statistical, Nonlinear, and Soft Matter Physics, 65(4 Pt 1), 041107.CrossRefGoogle Scholar
  31. 31.
    Hua, C. C., & Yu, C. C. (2007). Smoothed Periodogram of oxyhemoglobin saturation by pulse oximetry in sleep apnea syndrome: An automated analysis. Chest, 131(3), 750–757.CrossRefGoogle Scholar
  32. 32.
    Hu, K., Ivanov, P. C., Chen, Z., Carpena, P., & Stanley, H. E. (2001). Effect of trends on detrended fluctuation analysis. Physical Review E: Statistical, Nonlinear, and Soft Matter Physics, 64(1 Pt 1), 011114.CrossRefGoogle Scholar
  33. 33.
    Venables, W. N., & Ripley, B. D. (2002). Modern applied statistics with S (4th ed.). New York: Springer-Verlag.CrossRefGoogle Scholar
  34. 34.
    R Development Core & Team. (2010). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.Google Scholar
  35. 35.
    Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis: A global perspective. Upper Saddle River: Pearson Education Inc.Google Scholar
  36. 36.
    Schlosshan, D., & Elliott, M. W. (2004). Sleep. 3: Clinical presentation and diagnosis of the obstructive sleep apnoea hypopnoea syndrome. Thorax, 59(4), 347–352.CrossRefGoogle Scholar
  37. 37.
    Zamarron, C., Romero, P. V., Rodriguez, J. R., & Gude, F. (1999). Oximetry spectral analysis in the diagnosis of obstructive sleep apnoea. Clinical science (London, England), 97(4), 467–473.CrossRefGoogle Scholar
  38. 38.
    Sánchez-Morillo, D., López-Gordo, M., & León, A. (2014). Novel multiclass classification for home-based diagnosis of sleep apnea hypopnea syndrome. Expert Systems with Applications, 41(4), 1654–1662.CrossRefGoogle Scholar
  39. 39.
    Marcos, J. V., Hornero, R., Nabney, I. T., Álvarez, D., Gutiérrez-Tobal, G. C., & del Campo, F. (2016). Regularity analysis of nocturnal oximetry recordings to assist in the diagnosis of sleep apnoea syndrome. Medical Engineering & Physics, 38(3), 216–224.CrossRefGoogle Scholar
  40. 40.
    Gutiérrez-Tobal, G., Álvarez, D., Crespo, A., Arroyo, C., Vaquerizo-Villar, F., Barroso-García, V., et al. (2016). Multi-class adaboost to detect Sleep Apnea-Hypopnea Syndrome severity from oximetry recordings obtained at home. In 2016 Global Medical Engineering Physics Exchanges/Pan American Health Care Exchanges (GMEPE/PAHCE), 2016 (pp. 1–5): IEEEGoogle Scholar
  41. 41.
    Morillo, D. S., Gross, N., León, A., & Crespo, L. F. (2012). Automated frequency domain analysis of oxygen saturation as a screening tool for SAHS. Medical Engineering & Physics, 34(7), 946–953.CrossRefGoogle Scholar
  42. 42.
    Álvarez, D., Hornero, R., Marcos, J. V., & del Campo, F. (2012). Feature selection from nocturnal oximetry using genetic algorithms to assist in obstructive sleep apnoea diagnosis. Medical Engineering & Physics, 34(8), 1049–1057.CrossRefGoogle Scholar
  43. 43.
    Hang, L.-W., Yen, C.-W., & Lin, C.-L. (2012). Frequency-domain index of oxyhemoglobin saturation from pulse oximetry for obstructive sleep apnea syndrome. Journal of Medical and Biological Engineering, 32(5), 343–348.CrossRefGoogle Scholar

Copyright information

© Taiwanese Society of Biomedical Engineering 2017

Authors and Affiliations

  1. 1.Department of Internal MedicineChang Gung Memorial Hospital, Keelung & Chang Gung UniversityKeelungTaiwan

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