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Interdisciplinary Approaches to Automated Obstructive Sleep Apnea Diagnosis Through High-Dimensional Multiple Scaled Data Analysis

  • Giseon HeoEmail author
  • Kathryn Leonard
  • Xu Wang
  • Yi Zhou
Chapter
Part of the Association for Women in Mathematics Series book series (AWMS, volume 17)

Abstract

Obstructive sleep apnea (OSA) is a wide-spread condition that results in debilitating consequences including death. Diagnosis is a lengthy and expensive process because OSA is a multifactorial disorder, making it necessary to study many different types of data, including DNA sequences, multiple time series, metabolites, airflow in airway, and shape analysis of airway and patients’ faces. OSA data are an example of complex and multi-dimensional data for which analysis and interpretation can be challenging, requiring sophisticated analytic techniques. It may be no longer effective to independently apply methods from a specific discipline such as statistics, mathematics, or computing science. In this article, combining the analyses of three datasets from independent OSA studies, we illustrate the complementary nature of the techniques. Specifically, we apply techniques in statistics, machine learning, geometry, and computational topology to derive automated analytic tools for each data type. Taken together, these techniques provide a sophisticated diagnostic tool. A novel geometric OSA severity index (GSI) is developed using methods from computational geometry. This index measures the volume of the airway obstruction in OSA patients. The lower the GSI value is, the more severe the airway obstruction is. Persistent homology is employed to extract the importance information from 28-dimensional polysomnography (PSG) data. Random forests and principal component analysis are used and compared to identify important variables in the PSG, while logistic regression and random forest are used and compared to verify the prediction power of the identified variables. The results indicate that persistent homology can accurately extract importance information from PSG, and the identified important variables are meaningful for predicting obstructive apnea–hypopnea index (ahi). Cluster analysis is used to identify the pattern of the survey information, and the importance of responses to individual questions in survey questionnaires is also identified by random forest. The results from all three independent studies are very meaningful in clinical studies and can be used as guidance for clinical practitioners.

Notes

Acknowledgements

The authors would like to thank the Institute for Computational and Experimental Research Mathematics, the National Science Foundation (NSF-HRD 1500481), and the Association for Women in Mathematics for support, financial, and otherwise, of this collaboration. We thank the National Sleep Research Resource for their permission to use the dataset. We would like to thank the National Sciences and Engineering Research Council of Canada, Seed Grant from Women and Children’s Health Research Institute, University of Alberta, and Biomedical Research Award from American Association of Orthodontists Foundation. We would like to thank Facundo Mémoli for discussion on persistent homology.

References

  1. 1.
    W. Almuhammadi, K. Aboalayon, M. Faezipour, Efficient obstructive sleep apnea classification based on EEG signals, in 11th IEEE Long Island Systems, Applications and Technology Conference (LISAT) (2015). https://doi.org/10.1109/LISAT.2015.7160186
  2. 2.
    N. Alsufyani, A. Hess, N. Ray, P. Major, Segmentation of the nasal and pharyngeal airway using cone beam computed tomography part I: a new approach. Preprint (2017)Google Scholar
  3. 3.
    C. Avci, A. Akbaş, Sleep apnea classification based on respiration signals by using ensemble methods. Bio-Med. Mater. Eng. 26, S1703–S1710 (2015)CrossRefGoogle Scholar
  4. 4.
    S.M. Banabilh, A.H. Suzina, S. Dinsuhaimi, A.R. Samsudin, G.D. Singh, Craniofacial obesity in patients with obstructive sleep apnea. Sleep Breath. 13(1), 19–24 (2008)CrossRefGoogle Scholar
  5. 5.
    S. Bozkurt, A. Bostanci, M. Turhan, Can statistical machine learning algorithm help for classification of obstructive sleep apnea severity to optimal utilization of polysomnography resources? Methods Inf. Med. 56(4), 308–318 (2017)CrossRefGoogle Scholar
  6. 6.
    L. Breiman, Random forests. Mach. Learn. 45, 5–32 (2001)CrossRefGoogle Scholar
  7. 7.
    L. Breiman, A. Cutler, A. Liaw, Matthew Wiener: R packages “randomForest” (2015)Google Scholar
  8. 8.
    S.E. Brietzke, E.S. Katz, D.W. Roberson, Can history and physical examination reliably diagnose pediatric obstructive sleep apnea/hypopnea syndrome? A systematic review of the literature. Otolaryngol. Head Neck Surg. 131(6), 827–832 (2004)CrossRefGoogle Scholar
  9. 9.
    P.J. Brockwell, R.A. Davis, Time Series: Theory and Methods (Springer, Berlin, 2009)zbMATHGoogle Scholar
  10. 10.
    P. Bubenik, Statistical topological data analysis using persistence landscapes. J. Mach. Learn. Res. 16, 77–102 (2015)MathSciNetzbMATHGoogle Scholar
  11. 11.
    B. Caffo, M. Diener-West, N.M. Punjabi, J. Samet, A novel approach to prediction of mild obstructive sleep disordered breathing in a population-based sample: the sleep heart health study. Sleep, 33(12), 1641–1648 (2013)CrossRefGoogle Scholar
  12. 12.
    G.D.L. Canto, C. Pacheco-Pereira, S. Aydinoz, P.W. Major, C. Flores-Mir, D. Gozal, Diagnostic capability of biological markers in assessment of obstructive sleep apnea: a systematic review and meta-analysis. J. Clin. Sleep Med. 11(1), 27–36 (2015)Google Scholar
  13. 13.
    F. Chazal, B.T. Fasy, F. Lecci, B. Michel, A. Rinaldo, L. Wasserman, Subsampling methods for persistent homology, in International Conference on Machine Learning, pp. 2143–2151 (2015)Google Scholar
  14. 14.
    S. Chowdhury, Facundo Mëmoli, Persistent homology of directed networks, in 50th Asilomar Conference on Signals, Systems and Computers (IEEE, Piscataway, 2016), pp. 77–81. https://doi.org/10.1109/ACSSC.2016.7868997 Google Scholar
  15. 15.
    A. Collins, G. Zomorodian, A. Carlsson, L.J. Guibas, A barcode shape descriptor for curve point cloud data. Comput. Graph. 28, 881–894 (2004)CrossRefGoogle Scholar
  16. 16.
    A. Crespo, D. Álvarez, L. Kheirandish-Gozal, G.C. Gutiérrez-Tobal, A. Cerezo-Hernández, D. Gozal, R. Hornero, F. del Campo, Assessment of oximetry-based statistical classifiers as simplified screening tools in the management of childhood obstructive sleep apnea. Sleep Breath (2018). https://doi.org/10.1007/s11325-018-1637-3
  17. 17.
    A. Cutler, D. Richard Cutler, Tree-based methods, in High-Dimensional Data Analysis in Cancer Research. Part of the Series Applied Bioinformatics and Biostatistics in Cancer Research (Springer, New York, 2008), pp. 1–19Google Scholar
  18. 18.
    D.J. Eckert, D.P. White, A.S. Jordan, A. Malhotra, A. Wellman, Defining phenotypic causes of obstructive sleep apnea: identification of novel therapeutic targets. Am. J. Respir. Crit. Care Med. 188(8), 996–1004 (2013)CrossRefGoogle Scholar
  19. 19.
    H. Edelsbrunner, D. Letscher, A. Zomorodian, Topological persistence and simplification. Discret. Comput. Geom. 28, 511–533 (2002)MathSciNetCrossRefGoogle Scholar
  20. 20.
    H. Eldelsbrunner, E. Mucke, Three-dimensional alpha shapes. ACM Trans. Graphics 13(1), 43–72 (1994)CrossRefGoogle Scholar
  21. 21.
    B.T. Fasy, F. Lecci, Confidence sets for persistence diagrams. Ann. Stat. 42, 2301–2339 (2014)MathSciNetCrossRefGoogle Scholar
  22. 22.
    T.K. Ho, Random decision forests, in Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal, QC (IEEE, Piscataway, 1995), pp. 14–16, 278–282Google Scholar
  23. 23.
    G. James, D. Witten, T. Hastie, R. Tibshirani, An Introduction to Statistical Learning with Applications in R (Springer, New York, 2013)CrossRefGoogle Scholar
  24. 24.
    S. Jeong, W. Kim, S. Sung, Numerical investigation on the flow characteristics and aerodynamic force of the upper airway of patient with obstructive sleep apnea using computational fluid dynamics. Med. Eng. Phys. 29, 637–651 (2007)CrossRefGoogle Scholar
  25. 25.
    A. Jezzini, M. Ayache, A. Ibrahim, L. Elkhansa, ECG classification for sleep apnea detection, in Third International Conference on Advances in Biomedical Engineering (ICABME15) (2015). https://doi.org/10.1109/ICABME.2015.7323312
  26. 26.
    L. Kaufmann, P.J. Rousseeuw, Finding Groups in Data: An Introduction to Cluster Analysis (Wiley, New York, 1990)CrossRefGoogle Scholar
  27. 27.
    V. Kovacev-Nikolic, P. Bubenik, D. Nokolić, G. Heo, Using persistent homology and dynamical distances to analyze protein binding. Stat. Appl. Genet. Mol. Biol. 15(1), 19–38 (2016)MathSciNetCrossRefGoogle Scholar
  28. 28.
    Z.C. Lipton, D.C. Kale, C. Elkan, R. Wetzel, Learning to diagnose with LSTM recurrent neural networks. arXiv:1511.03677v7 (2015)Google Scholar
  29. 29.
    C.L. Marcus, L.J. Brooks, K.A. Draper, D. Gozal, A.C. Halbower, J. Jones, M.S. Schechter, S.H. Sheldon, K. Spruyt, S.D. Ward, C. Lehmann, R. Shiffman, Diagnosis and management of childhood obstructive sleep apnea syndrome. Am. Acad. Pediatr. 130, 576–584 (2012)Google Scholar
  30. 30.
    B.H. Menze, B.M.L. Kelm, R. Masuch, U. Himmelreich, P. Bachert, W. Petrich, F.A. Hamprecht, A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data. BMC Bioinf. 10(1), 213 (2009). https://doi.org/10.1186/1471-2105-10-213
  31. 31.
    R.B. Mitchell, S. Garetz, R.H. Moore, C.L. Rosen, C.L. Marcus, E.S. Katz, R. Arens, R.D. Chervin, S. Paruthi, R. Amin, L. Elden, S.S. Ellenberg, S. Redline, The use of clinical parameters to predict obstructive sleep apnea syndrome severity in children: the childhood adenotonsillectomy (CHAT) study randomized clinical trial. JAMA Otolaryngol. Head Neck Surg. 141(2), 130–136 (2015)CrossRefGoogle Scholar
  32. 32.
    MrOS-Visit2-PSG-Manual-of-Procedures.pdf. https://sleepdata.org/datasets/mros
  33. 33.
    S. Paruthi, C.L. Rosen, R. Wang, J. Weng, C.L. Marcus, R.D. Chervin, J.J. Stanley, E.S. Katz, R. Amin, S. Redline, End-tidal carbon dioxide measurement during pediatric polysomnography: signal quality, association with apnea severity, and prediction of neurobehavioral outcomes. Sleep 38(11), 1719–1726 (2015)CrossRefGoogle Scholar
  34. 34.
    P. Petrov, S.T. Rush, Z. Zhai, C.H. Lee, P.T. Kim, G. Heo, Topological data analysis of Clostridioides difficile infection and fecal microbiota transplantation. arXiv:1707.08774v2 (2017)Google Scholar
  35. 35.
    S. Redline, Obstructive sleep apnea-hypopnea and incident stroke: the sleep heart health study. Am. J. Respir. Crit. Care Med. 2, 269–277 (2010)CrossRefGoogle Scholar
  36. 36.
    J.S. Reininghause, S. Huber, U. Bauer, R. Kwitt, A stable multi-scale kernel for topological machine learning, in Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR’15), Boston, MA (2015), pp. 4741–4748CrossRefGoogle Scholar
  37. 37.
    A. Roebuck, G.D. Clifford, Comparison of standard and novel signal analysis approaches to obstructive sleep apnea classification. Front. Bioeng. Biotechnol. 3, 114 (2015)CrossRefGoogle Scholar
  38. 38.
    L. Rokach, O. Maimon, Clustering methods, in Data Mining and Knowledge Discovery Handbook (Springer, Boston, 2005), pp. 321–352CrossRefGoogle Scholar
  39. 39.
    S. Ryali, T. Chen, K. Supekar, V. Menon, Estimation of functional connectivity in fMRI data using stability selection-based sparse partial correlation with elastic net penalty. NeuroImage 59, 3852–3861 (2012)CrossRefGoogle Scholar
  40. 40.
    P. Su, X-R. Ding, Y-T. Zhang, J. Liu, F. Miao, N. Zhao, Long-term blood pressure prediction with deep recurrent neural networks. arXiv:1705.04524v3 (2017)Google Scholar
  41. 41.
    C. Van Holsbeke, W. Vos, K. Van Hoorenbeeck, A. Boudewyns, R. Salgado, P.R. Verdonck, J. Ramet, J. De Backer, W. De Backer, S.L. Verhulst, Functional respiratory imaging as a tool to assess upper airway patency in children with obstructive sleep apnea. Sleep Med. 14, 433–439 (2013)CrossRefGoogle Scholar
  42. 42.
    V. Varvarigou, I.J. Dahabreh, A. Malhotra, S.N. Kales, A review of genetic association studies of obstructive sleep apnea: field synopsis and meta-analysis. Sleep 34(11), 1461–1468 (2011)CrossRefGoogle Scholar
  43. 43.
    A. Zomorodian, G. Carlsson, Computing persistent homology. Discret. Comput. Geom. 33, 249–274 (2005)MathSciNetCrossRefGoogle Scholar

Copyright information

© The Author(s) and the Association for Women in Mathematics 2019

Authors and Affiliations

  1. 1.School of DentistryUniversity of AlbertaEdmontonCanada
  2. 2.Department of Computer ScienceOccidental CollegeLos AngelesUSA
  3. 3.Department of MathematicsWilfrid Laurier UniversityWaterlooCanada
  4. 4.Department of Mathematical and Statistical SciencesUniversity of AlbertaEdmontonCanada

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