- About this Journal
- Abstracting and Indexing
- Aims and Scope
- Annual Issues
- Article Processing Charges
- Articles in Press
- Author Guidelines
- Bibliographic Information
- Citations to this Journal
- Contact Information
- Editorial Board
- Editorial Workflow
- Free eTOC Alerts
- Publication Ethics
- Reviewers Acknowledgment
- Submit a Manuscript
- Subscription Information
- Table of Contents
Computational and Mathematical Methods in Medicine
Volume 2012 (2012), Article ID 947191, 8 pages
doi:10.1155/2012/947191
Two-Dimensional Matrix Algorithm Using Detrended Fluctuation Analysis to Distinguish Burkitt and Diffuse Large B-Cell Lymphoma
1Department of Mechanical Engineering, Yuan Ze University, 135 Yuan-Tung Road, Chungli 32003, Taiwan
2Department of Pathology, National Taiwan University Hospital, Taipei 100, Taiwan
3School of Engineering and Design, Brunel University, London UB8 3PH, UK
4Center for Dynamical Biomarkers and Translational Medicine, National Central University, Chungli 32001, Taiwan
Received 19 September 2012; Accepted 19 November 2012
Academic Editor: Wenxiang Cong
Copyright © 2012 Rong-Guan Yeh et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Linked References
- B. Mandelbrot, “The variation of certain speculative prices,” The Journal of Business, vol. 36, no. 4, pp. 394–419, 1963. View at Publisher · View at Google Scholar
- R. Lopes and N. Betrouni, “Fractal and multifractal analysis: a review,” Medical Image Analysis, vol. 13, no. 4, pp. 634–649, 2009. View at Publisher · View at Google Scholar · View at Scopus
- W. Klonowski, “Signal and image analysis using chaos theory and fractal geometry,” Machine Graphics and Vision Journal, vol. 9, pp. 403–432, 2000.
- O. Zmeškal, M. Veselý, M. Nežádal, and M. Buchníček, “Fractal Analysis of Image Structures,” Harmonic and Fractal Image Analysis, pp. 3–5, 2001.
- C. K. Peng, S. V. Buldyrev, S. Havlin, M. Simons, H. E. Stanley, and A. L. Goldberger, “Mosaic organization of DNA nucleotides,” Physical Review E, vol. 49, no. 2, pp. 1685–1689, 1994. View at Publisher · View at Google Scholar · View at Scopus
- C. K. Peng, S. Havlin, H. E. Stanley, and A. L. Goldberger, “Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series,” Chaos, vol. 5, no. 1, pp. 82–87, 1995. View at Scopus
- R. G. Yeh, J. S. Shieh, G. Y. Chen, and C. D. Kuo, “Detrended fluctuation analysis of short-term heart rate variability in late pregnant women,” Autonomic Neuroscience, vol. 150, no. 1-2, pp. 122–126, 2009. View at Publisher · View at Google Scholar · View at Scopus
- H. E. Stanley, L. A. N. Amaral, A. L. Goldberger, S. Havlin, P. C. Ivanov, and C. K. Peng, “Statistical physics and physiology: monofractal and multifractal approaches,” Physica A, vol. 270, no. 1, pp. 309–324, 1999. View at Publisher · View at Google Scholar · View at Scopus
- R. G. Yeh, G. Y. Chen, J. S. Shieh, and C. D. Kuo, “Parameter investigation of detrended fluctuation analysis for short-term human heart rate variability,” Journal of Medical and Biological Engineering, vol. 30, no. 5, pp. 277–282, 2010. View at Publisher · View at Google Scholar · View at Scopus
- T. Nakamura, H. Horio, S. Miyashita, Y. Chiba, and S. Sato, “Identification of development and autonomic nerve activity from heart rate variability in preterm infants,” BioSystems, vol. 79, no. 1–3, pp. 117–124, 2005. View at Publisher · View at Google Scholar · View at Scopus
- N. G. Mahon, A. E. Hedman, M. Padula et al., “Fractal correlation properties of R-R interval dynamics in asymptomatic relatives of patients with dilated cardiomyopathy,” European Journal of Heart Failure, vol. 4, no. 2, pp. 151–158, 2002. View at Publisher · View at Google Scholar · View at Scopus
- T. H. Mäkikallio, J. Koistinen, L. Jordaens et al., “Heart rate dynamics before spontaneous onset of ventricular fibrillation in patients with healed myocardial infarcts,” American Journal of Cardiology, vol. 83, no. 6, pp. 880–884, 1999. View at Publisher · View at Google Scholar · View at Scopus
- C. K. Peng, S. V. Buldyrev, A. L. Goldberger et al., “Long-range correlations in nucleotide sequences,” Nature, vol. 356, no. 6365, pp. 168–170, 1992. View at Publisher · View at Google Scholar · View at Scopus
- S. Bahar, J. W. Kantelhardt, A. Neiman et al., “Long-range temporal anti-correlations in paddlefish electroreceptors,” Europhysics Letters, vol. 56, no. 3, pp. 454–460, 2001. View at Publisher · View at Google Scholar · View at Scopus
- S. Blesić, S. Milošević, D. Stratimirović, and M. Ljubisavljević, “Detrended fluctuation analysis of time series of a firing fusimotor neuron,” Physica A, vol. 268, no. 3-4, pp. 275–282, 1999. View at Publisher · View at Google Scholar
- J. M. Hausdorff, S. L. Mitchell, R. Firtion et al., “Altered fractal dynamics of gait: reduced stride-interval correlations with aging and Huntington's disease,” Journal of Applied Physiology, vol. 82, no. 1, pp. 262–269, 1997. View at Scopus
- J. M. Lee, D. J. Kim, I. Y. Kim, K. S. Park, and S. I. Kim, “Detrended fluctuation analysis of EEG in sleep apnea using MIT/BIH polysomnography data,” Computers in Biology and Medicine, vol. 32, no. 1, pp. 37–47, 2002. View at Publisher · View at Google Scholar · View at Scopus
- J. M. Lee, D. J. Kim, I. Y. Kim, K. Suk Park, and S. I. Kim, “Nonlinear-analysis of human sleep EEG using detrended fluctuation analysis,” Medical Engineering and Physics, vol. 26, no. 9, pp. 773–776, 2004. View at Publisher · View at Google Scholar · View at Scopus
- M. Staudacher, S. Telser, A. Amann, H. Hinterhuber, and M. Ritsch-Marte, “A new method for change-point detection developed for on-line analysis of the heart beat variability during sleep,” Physica A, vol. 349, no. 3-4, pp. 582–596, 2005. View at Publisher · View at Google Scholar · View at Scopus
- C. J. Stam, T. Montez, B. F. Jones et al., “Disturbed fluctuations of resting state EEG synchronization in Alzheimer's disease,” Clinical Neurophysiology, vol. 116, no. 3, pp. 708–715, 2005. View at Publisher · View at Google Scholar · View at Scopus
- P. Grau-Carles, “Long-range power-law correlations in stock returns,” Physica A, vol. 299, no. 3-4, pp. 521–527, 2001. View at Publisher · View at Google Scholar · View at Scopus
- R. Nagarajan and R. G. Kavasseri, “Minimizing the effect of periodic and quasi-periodic trends in detrended fluctuation analysis,” Chaos, Solitons and Fractals, vol. 26, no. 3, pp. 777–784, 2005. View at Publisher · View at Google Scholar · View at Scopus
- R. Weron, “Estimating long-range dependence: finite sample properties and confidence intervals,” Physica A, vol. 312, no. 1-2, pp. 285–299, 2002. View at Publisher · View at Google Scholar · View at Scopus
- R. G. Yeh, J. S. Shieh, Y. Y. Han, Y. J. Wang, and S. C. Tseng, “Detrended fluctuation analyses of short-term heart rate variability in surgical intensive care units,” Biomedical Engineering, vol. 18, no. 2, pp. 67–72, 2006. View at Publisher · View at Google Scholar · View at Scopus
- R. G. Yeh, Y. Y. Han, J. S. Shieh, Y. J. Wang, S. C. Tseng, and Y. C. Fu, “Nonrandomness index applied for heart rate variability in surgical intensive care units using frequency and rank order statistics,” Biomedical Engineering, vol. 19, pp. 303–311, 2007. View at Publisher · View at Google Scholar
- G. F. Gu and W. X. Zhou, “Detrended fluctuation analysis for fractals and multifractals in higher dimensions,” Physical Review E, vol. 74, no. 6, Article ID 061104, 2006. View at Publisher · View at Google Scholar
- W. X. Zhou, “Multifractal detrended cross-correlation analysis for two nonstationary signals,” Physical Review E, vol. 77, no. 6, Article ID 066211, 2008. View at Publisher · View at Google Scholar
- K. Dong and P. Shang, “Statistical properties of detrended cross-correlation analysis,” Journal of Beijing Jiaotong University, vol. 34, no. 6, pp. 64–67, 2010. View at Scopus
- B. Podobnik and H. E. Stanley, “Detrended cross-correlation analysis: a new method for analyzing two nonstationary time series,” Physical Review Letters, vol. 100, no. 8, Article ID 084102, 2008. View at Publisher · View at Google Scholar · View at Scopus
- Z. Q. Jiang and W. X. Zhou, “Multifractal detrending moving-average cross-correlation analysis,” Physical Review E, vol. 84, no. 1, Article ID 016106, 2011. View at Publisher · View at Google Scholar
- T. Ando, M. Suguro, T. Hanai, T. Kobayashi, H. Honda, and M. Seto, “Fuzzy neural network applied to gene expression profiling for predicting the prognosis of diffuse large B-cell Lymphoma,” Japanese Journal of Cancer Research, vol. 93, no. 11, pp. 1207–1212, 2002. View at Scopus
- G. Wright, B. Tan, A. Rosenwald, E. H. Hurt, A. Wiestner, and L. M. Staudt, “A gene expression-based method to diagnose clinically distinct subgroups of diffuse large B cell lymphoma,” Proceedings of the National Academy of Sciences of the United States of America, vol. 100, no. 17, pp. 9991–9996, 2003. View at Publisher · View at Google Scholar · View at Scopus
- C. P. Hans, D. D. Weisenburger, T. C. Greiner et al., “Confirmation of the molecular classification of diffuse large B-cell lymphoma by immunohistochemistry using a tissue microarray,” Blood, vol. 103, no. 1, pp. 275–282, 2004. View at Publisher · View at Google Scholar · View at Scopus
- C. Bellan, L. Stefano, D. F. Giulia, E. A. Rogena, and L. Lorenzo, “Burkitt lymphoma versus diffuse large B-cell lymphoma: a practical approach,” Hematological Oncology, vol. 28, no. 2, pp. 53–56, 2010. View at Publisher · View at Google Scholar · View at Scopus
- N. Nakamura, H. Nakamine, J. I. Tamaru et al., “The distinction between Burkitt lymphoma and diffuse large B-cell lymphoma with c-myc rearrangement,” Modern Pathology, vol. 15, no. 7, pp. 771–776, 2002. View at Publisher · View at Google Scholar · View at Scopus
- X. F. Zhao, A. Hassan, A. Perry, Y. Ning, S. A. Stass, and L. P. Dehner, “C-MYC rearrangements are frequent in aggressive mature B-cell lymphoma with atypical morphology,” International Journal of Clinical and Experimental Pathology, vol. 1, no. 1, pp. 65–74, 2008.
- X. Cui, C. W. Lin, M. F. Abbod, Q. Liu, and J. S. Shieh, “Diffuse large B-cell lymphoma classification using linguistic analysis and ensembled artificial neural networks,” Journal of the Taiwan Institute of Chemical Engineers, vol. 43, no. 1, pp. 15–23, 2012. View at Publisher · View at Google Scholar
- A. L. Goldberger, L. A. N. Amaral, J. M. Hausdorff, P. C. Ivanov, C. K. Peng, and H. E. Stanley, “Fractal dynamics in physiology: alterations with disease and aging,” Proceedings of the National Academy of Sciences of the United States of America, vol. 99, no. 1, pp. 2466–2472, 2002. View at Publisher · View at Google Scholar · View at Scopus
- T. H. Mäkikallio, T. Seppänen, K. E. J. Airaksinen et al., “Dynamic analysis of heart rate may predict subsequent ventricular tachycardia after myocardial infarction,” American Journal of Cardiology, vol. 80, no. 6, pp. 779–783, 1997. View at Publisher · View at Google Scholar · View at Scopus
- T. H. Mäkikallio, T. Ristimäe, K. E. J. Airaksinen, C. K. Peng, A. L. Goldberger, and H. V. Huikuri, “Heart rate dynamics in patients with stable angina pectoris and utility of fractal and complexity measures,” American Journal of Cardiology, vol. 81, no. 1, pp. 27–31, 1998. View at Publisher · View at Google Scholar · View at Scopus
- A. Carbone, “Algorithm to estimate the Hurst exponent of high-dimensional fractals,” Physical Review E, vol. 76, no. 5, Article ID 056703, 2007. View at Publisher · View at Google Scholar
- G. F. Gu and W. X. Zhou, “Detrending moving average algorithm for multifractals,” Physical Review E, vol. 82, no. 1, Article ID 011136, 2010. View at Publisher · View at Google Scholar