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Irregular Breathing Classification from Multiple Patient Datasets

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Prediction and Classification of Respiratory Motion

Part of the book series: Studies in Computational Intelligence ((SCI,volume 525))

Abstract

Complicated breathing behaviors including uncertain and irregular patterns can affect the accuracy of predicting respiratory motion for precise radiation dose delivery [1–6]. So far investigations on irregular breathing patterns have been limited to respiratory monitoring of only extreme inspiration and expiration [7]. Using breathing traces acquired on a Cyberknife treatment facility, we retrospectively categorized breathing data into several classes based on the extracted feature metrics derived from breathing data of multiple patients. The novelty of this study is that the classifier using neural networks can provide clinical merit for the statistically quantitative modeling of irregular breathing motion based on a regular ratio representing how many regular/irregular patterns exist within an observation period. We propose a new approach to detect irregular breathing patterns using neural networks, where the reconstruction error can be used to build the distribution model for each breathing class. The sensitivity, specificity and Receiver operating characteristic (ROC) curve of the proposed irregular breathing pattern detector was analyzed. The experimental results of 448 patients’ breathing patterns validated the proposed irregular breathing classifier.

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References

  1. K. Malinowski, T.J. McAvoy, R. George, S. Dietrich, W.D. D’Souza, Incidence of changes in respiration-induced tumor motion and its relationship with respiratory surrogates during individual treatment fractions. Int. J. Radiat. Oncol. Biol. Phys. 82(5), 1665–1673 (2012)

    Google Scholar 

  2. M.J. Murphy, D. Pokhrel, Optimization of an adaptive neural network to predict breathing. Med. Phys. 36(1), 40–47 (2009)

    Article  Google Scholar 

  3. M.J. Murphy, S. Dieterich, Comparative performance of linear and nonlinear neural networks to predict irregular breathing. Phys. Med. Biol. 51(22), 5903–5914 (2006)

    Article  Google Scholar 

  4. S.S. Vedam, P.J. Keall, A. Docef, D.A. Todor, V.R. Kini, R. Mohan, Predicting respiratory motion for four-dimensional radiotherapy. Med. Phys. 31(8), 2274–2283 (2004)

    Article  Google Scholar 

  5. I. Buzurovic, K. Huang, Y. Yu, T.K. Podder, A robotic approach to 4D real-time tumor tracking for radiotherapy. Phys. Med. Biol. 56(5), 1299–1318 (2011)

    Article  Google Scholar 

  6. P.S. Verma, H. Wu, M.P. Langer, I.J. Das and G. Sandison, Survey: real-time tumor motion prediction for image guided radiation treatment. Comput. Sci. Eng. 13(5), 24–35 (2011)

    Google Scholar 

  7. W. Lu, M.M. Nystrom, P.J. Parikh, D.R. Fooshee, J.P. Hubenschmidt, J.D. Bradley, D.A. Low, A semi-automatic method for peak and valley detection in free-breathing respiratory waveforms. Med. Phys. 33(10), 3634–3636 (2006)

    Article  Google Scholar 

  8. P.J. Keall, G.S. Mageras, J.M. Balter, R.S. Emery, K.M. Forster, S.B. Jiang, J.M. Kapatoes, D.A. Low, M.J. Murphy, B.R. Murray, C.R. Ramsey, M.B. Van Herk, S.S. Vedam, J.W. Wong, E. Yorke, The management of respiratory motion in radiation oncology report of AAPM Task Group 76. Med. Phys. 33(10), 3874–3900 (2006)

    Article  Google Scholar 

  9. H. Murshed, H.H. Liu, Z. Liao, J.L. Barker, X. Wang, S.L. Tucker, A. Chandra, T. Guerrero, C. Stevens, J.Y. Chang, M. Jeter, J.D. Cox, R. Komaki, R. Mohan, Dose and volume reduction for normal lung using intensity-modulated radiotherapy for advanced-stage non-small-cell lung cancer. Int. J. Radiat. Oncol. Biol. Phys. 58(4), 1258–1267 (2004)

    Article  Google Scholar 

  10. R.I. Berbeco, S. Nishioka, H. Shirato, G.T.Y. Chen, S.B. Jiang, Residual motion of lung tumours in gated radiotherapy with external respiratory surrogates. Phys. Med. Biol. 50(16), 3655–3667 (2005)

    Article  Google Scholar 

  11. Y.D. Mutaf, C.J. Scicutella, D. Michalski, K. Fallon, E.D. Brandner, G. Bednarz, M.S. Huq, A simulation study of irregular respiratory motion and its dosimetric impact on lung tumors. Phys. Med. Biol. 56(3), 845–859 (2011)

    Article  Google Scholar 

  12. L. Huang, K. Park, T. Boike, P. Lee, L. Papiez, T. Solberg, C. Ding, R.D. Timmerman, A study on the dosimetric accuracy of treatment planning for stereotactic body radiation therapy of lung cancer using average and maximum intensity projection images. Radiother. Oncol. 96(1), 48–54 (2010)

    Article  Google Scholar 

  13. G. Benchetrit, Breathing pattern in humans: diversity and individuality. Respir. Physiol. 122(2–3), 123–129 (2000)

    Article  Google Scholar 

  14. A. Cohen, D. Landsberg, Analysis and automatic classification of breath sounds. IEEE Trans. Biomed. Eng. 31(9), 585–590 (1984)

    Article  Google Scholar 

  15. M.J. Baemani, A. Monadjemi, P. Moallem, Detection of respiratory abnormalities using artificial neural networks. J Comput. Sci. 4(8), 663–667 (2008)

    Article  Google Scholar 

  16. S. Jafari, H. Arabalibeik, K. Agin, Classification of normal and abnormal respiration patterns using flow volume curve and neural network, in International Symposium on Health Informatics and Bioinformatics, pp. 110–113, 2010

    Google Scholar 

  17. I. Ayappa, R.G. Norman, D. Whiting, A.H.W. Tsai, F. Anderson, E. Donnely, D.J. Silberstein, D.M. Rapoport, Irregular respiration as a marker of wakefulness during titration of CPAP. Sleep 32(1), 99–104 (2009)

    Google Scholar 

  18. T. Mu, T.C. Pataky, A.H. Findlow, M.S.H. Aung, J.Y. Goulermas, Automated nonlinear feature generation and classification of foot pressure lesions. IEEE Trans. Inf Technol. Biomed. 14(2), 418–424 (2010)

    Article  Google Scholar 

  19. H. Atoui, J. Fayn, P. Rubel, A novel neural-network model for deriving standard 12-lead ecgs from serial three-lead ECGS: application to self-care. IEEE Trans. Inf Technol. Biomed. 14(3), 883–890 (2010)

    Article  Google Scholar 

  20. P. Chazal, M. O’Dwyer, R.B. Reilly, Automatic classification of heartbeats using ECG morphology and heartbeat interval features. IEEE Trans. Biomed. Eng. 51(7), 1196–1206 (2004)

    Article  Google Scholar 

  21. P. Chazal, R.B. Reilly, A patient-adapting heartbeat classifier using ECG morphology and heartbeat interval features. IEEE Trans. Biomed. Eng. 53(12), 2535–2543 (2006)

    Article  Google Scholar 

  22. C.I. Christodoulou, C.S. Pattichis, M. Pantziaris, A. Nicolaides, Texture-based classification of atherosclerotic carotid plaques. IEEE Trans. Med. Imag. 22(7), 902–912 (2003)

    Article  Google Scholar 

  23. W. Chen, C.E. Metz, M.L. Giger, K. Drukker, A novel hybrid linear/nonlinear classifier for two-class classification: Theory, algorithm, and applications. IEEE Trans. Med. Imag. 29(2), 428–441 (2010)

    Article  Google Scholar 

  24. A.H. Khandoker, J. Gubbi, M. Palaniswami, Automated scoring of obstructive sleep apnea and hypopnea events using short-term electrocardiogram recordings. IEEE Trans. Inf Technol. Biomed. 13(6), 1057–1067 (2009)

    Article  Google Scholar 

  25. M. Hoogeman, J.B. Prévost, J. Nuyttens, J. Pöll, P. Levendag, B. Heijmen, Clinical accuracy of the respiratory tumor tracking system of the cyberknife: Assessment by analysis of log files. Int. J. Radiat. Oncol. Biol. Phys. 74(1), 297–303 (2009)

    Article  Google Scholar 

  26. S.J. Lee, Y. Motai, M. Murphy, Respiratory motion estimation with hybrid implementation of extended Kalman filter. IEEE Trans. Ind. Electron. 59(11), 4421–4432 (2012)

    Google Scholar 

  27. W. Bai, S.M. Brady, Motion correction and attenuation correction for respiratory gated PET images. IEEE Trans. Med. Imag. 30(2), 351–365 (2011)

    Article  Google Scholar 

  28. J. Ehrhardt, R. Werner, A. Schmidt-Richberg, H. Handels, Statistical modeling of 4D respiratory lung motion using diffeomorphic image registration. IEEE Trans. Med. Imag. 30(2), 251–265 (2011)

    Article  Google Scholar 

  29. A.P. King, K.S. Rhode, R.S. Razavi, T.R. Schaeffter, An adaptive and predictive respiratory motion model for image-guided interventions: theory and first clinical application. IEEE Trans. Med. Imag. 28(12), 2020–2032 (2009)

    Article  Google Scholar 

  30. A. Lanatà, E.P. Scilingo, E. Nardini, G. Loriga, R. Paradiso, D. De-Rossi, Comparative evaluation of susceptibility to motion artifact in different wearable systems for monitoring respiratory rate. IEEE Trans. Inf Technol. Biomed. 14(2), 378–386 (2010)

    Article  Google Scholar 

  31. F. Pernkopf, D. Bouchaffra, Genetic-based EM algorithm for learning Gaussian mixture models. IEEE Trans. Pattern Anal. Mach. Intell. 27(8), 1344–1348 (2005)

    Article  Google Scholar 

  32. P. Guo, P. Guo, M.R. Lyu, Cluster number selection for a small set of samples using the bayesian Ying-Yang model. IEEE Trans. Neural Netw. 13(3), 757–763 (2002)

    Article  Google Scholar 

  33. J. Zhao, P.L.H. Yu, Fast ML estimation for the mixture of factor analyzers via an ECM algorithm. IEEE Trans. Neural Netw. 19(11), 1956–1961 (2008)

    Article  Google Scholar 

  34. V. Chandola, A. Banerjee, V. Kumar, Anomaly detection: A survey. ACM Comput. Surv. 41(3), 5:1–15:58 (2009)

    Google Scholar 

  35. M.A. Kupinski, D.C. Edwards, M.L. Giger, C.E. Metz, Ideal observer approximation using bayesian classification neural networks. IEEE Trans. Med. Imag. 20(9), 886–899 (2001)

    Article  Google Scholar 

  36. V. Srinivasan, C. Eswaran, N. Sriraam, Approximate entropy-based epileptic EEG detection using artificial neural networks. IEEE Trans. Inf Technol. Biomed. 11(3), 288–295 (2007)

    Article  Google Scholar 

  37. S. Haykin, Neural Networks and Learning Machines, 3rd edn. (Pearson, New York, 2009)

    Google Scholar 

  38. R.O. Duda, P.E. Hart, D.G. Stork, Pattern Classification (Wiley, New York, 2001)

    Google Scholar 

  39. Z. Li, D. Lin, X. Tang, Nonparametric discriminant analysis for face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 31(4), 755–761 (2009)

    Article  Google Scholar 

  40. T. Fawcett, An introduction to ROC analysis. Pattern Recogn. Lett. 27(8), 861–874 (2006)

    Article  MathSciNet  Google Scholar 

  41. K.P. Burnham, D.R. Anderson, Model Selection and Multi-Model Inference: A Practical Information-Theoretic Approach (Springer-Verlag, New York, 2002)

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, Texas A&M University—Texarkana, Texarkana, TX, USA

    Suk Jin Lee

  2. Department of Electrical and Computer Engineering, Virginia Commonwealth University, Richmond, VA, USA

    Yuichi Motai

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  1. Suk Jin Lee
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  2. Yuichi Motai
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Correspondence to Suk Jin Lee .

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Lee, S.J., Motai, Y. (2014). Irregular Breathing Classification from Multiple Patient Datasets. In: Prediction and Classification of Respiratory Motion. Studies in Computational Intelligence, vol 525. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41509-8_6

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  • DOI: https://doi.org/10.1007/978-3-642-41509-8_6

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41508-1

  • Online ISBN: 978-3-642-41509-8

  • eBook Packages: EngineeringEngineering (R0)

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