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

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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.

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References

  1. Caples, S. M., Gami, A. S., & Somers, V. K. (2005). Obstructive sleep apnea. Annals of Internal Medicine, 142(3), 187–197.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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. 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.

    Article  Google Scholar 

  16. Suki, B. (2002). Fluctuations and power laws in pulmonary physiology. American Journal of Respiratory and Critical Care Medicine, 166(2), 133–137.

    Article  Google Scholar 

  17. Seely, A. J., & Macklem, P. T. (2004). Complex systems and the technology of variability analysis. Critical Care, 8(6), R367–R384.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  33. Venables, W. N., & Ripley, B. D. (2002). Modern applied statistics with S (4th ed.). New York: Springer-Verlag.

    Book  Google Scholar 

  34. R Development Core & Team. (2010). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.

    Google Scholar 

  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. Schlosshan, D., & Elliott, M. W. (2004). Sleep. 3: Clinical presentation and diagnosis of the obstructive sleep apnoea hypopnoea syndrome. Thorax, 59(4), 347–352.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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): IEEE

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

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Correspondence to Chung-Ching Hua.

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Hua, CC., Yu, CC. Detrended Fluctuation Analysis of Oxyhemoglobin Saturation by Pulse Oximetry in Sleep Apnea Syndrome. J. Med. Biol. Eng. 37, 791–799 (2017). https://doi.org/10.1007/s40846-017-0251-3

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  • DOI: https://doi.org/10.1007/s40846-017-0251-3

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