COMPLEXITY ANALYSIS OF MASS SPECTROMETRY DATA FOR DISEASE CLASSIFICATION USING GA-BASED MULTISCALE ENTROPY

Cuong C. To, Tuan D. Pham

2011

Abstract

Entropy methods including approximate entropy (ApEn), sample entropy (SampEn) and multiscale entropy (MSE) have recently been applied to measure the complexity of finite length time series for classification of diseases. In order to effectively use these entropy methods, parameters such as m, r, and scale factor (in MSE) are to be determined. So far, there have been no general rules to select these parameters as they depend on particular problems. In this paper, we introduce a genetic algorithm (GA) based method for optimal selection of these parameters in a sense that the entropic difference between healthy and pathologic groups are maximized.

References

  1. Burioka N., Cornélissen G., et al., 2003. Approximate entropy of human respiratory movement during eyeclosed waking and different sleep stages. Chest, 123: 80-86.
  2. Burioka N., Cornelissen G., et al., 2005. Approximate entropy of the electroencephalogram in healthy awake subjects and absence epilepsy patients. Clin. EEG Neurosci, 36:188-193.
  3. Caldirola D., Bellodi L., et al., 2004. Approximate Entropy of Respiratory Patterns in Panic Disorder. Am. J. Psychiatry, 161:79-87.
  4. Calegari P., Guidec F., et al., 1997. Parallel island-based genetic algorithm for radio network design. Journal of Parallel and Distributed Computing, 47(1): 86-90.
  5. Cantu-Paz E., 2001. Efficient and accurate parallel genetic algorithms. Kluwer Academic Publishers.
  6. Castiglioni P. and Di Rienzo M., 2008. How the threshold “r” influences approximate entropy analysis of heartrate variability. Computers in Cardiology, 35:561-564.
  7. Chong K. P. E and Zak H. S., 2001. An Introduction to Optimization, John Wiley & Sons, New York.
  8. Conrads T. P. and Zhou M., et al, 2003. Cancer diagnosis using proteomic patterns. Expert Rev. Mol. Diagn., 3: 411-420.
  9. Costa M. and Goldberger A. L., 2002. C.K. Peng, Multiscale entropy analysis of complex physiologic time series. Phys. Rev. Lett., 89.
  10. Costa M., Goldberger A. L., Peng C. K., 2005. Multiscale entropy analysis of biological signals. Phys Rev E Stat Nonlin Soft Matter Phys., 71.
  11. Costa M., Goldberger A. L., Peng C. K., 2002. Multiscale entropy to distinguish physiologic and synthetic RR time series. Computers in Cardiology, 29:137-140.
  12. Eckmann J. P. and Ruelle D., 1985. Ergodic theory of chaos and strange attractors. Rev. Modern Phys., 57:617-654.
  13. Eidhammer I., Flikka K., et al., 2007. Computational methods for mass spectrometry proteomics, Wiley.
  14. Ferenets R., Lipping Tarmo, et al., 2006. Comparison of entropy and complexity measures for the assessment of depth of sedation. IEEE Trans. Biomed. Eng., 53:1067-1077.
  15. Fernandez de Vega F., 2005. Parallel genetic programming. Workshop 2005 IEEE Congress on Evolutionary Computation.
  16. Hagan M. T., Demuth H. B., Beale M. H., 1995. Neural Network Design, PWS Pub. Co..
  17. Ho K. K., Moody G. B., 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: 842-848.
  18. Hornero R., Aboy M., et al., 2005. Interpretation of Approximate Entropy: Analysis of Intracranial Pressure Approximate Entropy During Acute Intracranial Hypertension. IEEE Trans. Biomed. Eng., 52:1671-1680.
  19. Kim W. S., Yoon Y. Z., et al., 2005. Nonlinear characteristics of heart rate time series: influence of three recumbent positions in patients with mild or severe coronary artery disease. Physiol. Meas., 26:517-529.
  20. Koskinen M., Seppanen T., et al., 2006. Monotonicity of approximate entropy during transition from awareness to unresponsiveness due to propofol anesthetic induction. IEEE Trans. Biomed. Eng., 53:669-675.
  21. Lake D. E., Richman J. S., et al., 2002. Sample entropy analysis of neonatal heart rate variability. Am. J. Physiol Regul Integr Comp Physiol, 283:789-797.
  22. Lee M-L. T., 2004. Analysis of microarray gene expression data, Kluwer Academic Publishers, Boston.
  23. Lu S., Chen X., et al., 2008. Automatic selection of the threshold value r for approximate entropy. IEEE Trans. Biomed. Eng., 55: 1966-1972.
  24. Mitchell M., 2001. An Introduction to Genetic Algorithm, MIT Press, London.
  25. Muniyappa R., Sorkin J. D., et al., 2007. Long-term testosterone supplementation augments overnight growth hormone secretion in healthy older men. Am. J. Physiol Endocrinol Metab, 293: 769-775.
  26. Petricoin E. F., Ardekani A. M., et al., 2002. Use of proteomic patterns in serum to identify ovarian cancer. The Lancet, 359:572-577.
  27. Pham T. D., Wang H., et al., 2008. Computational prediction models for early detection of risk of cardiovascular events using mass spectrometry data. IEEE Trans.ITB 12:636-643.
  28. Pincus S. M., 1991. Approximate entropy as a measure of system complexity. Proc. Natl. Acad. Sci. USA, 88: 2297-2301.
  29. Richman J. S. and Moorman J. R., 2000. Physiological time-series analysis using approximate entropy and sample entropy. Am. J. Physiol Heart Circ Physiol 278: 2039-2049.
  30. Rukhin A. L., 2000. Approximate entropy for testing randomness. J. Appl. Probability, 37:88-100.
  31. Sastry K. and Goldberg D. E., 2000. On extended compact genetic algorithm. GECCO 2000.
  32. Seeger A., 2006. Recent Advances in Optimization, Springer, Berlin.
  33. To C., Vohradsky J., 2007. A parallel genetic algorithm for single class pattern classification and its application for gene expression profiling in streptomyces coelicolor. BMC Genomics, 8:49.
  34. To C., Vohradsky J., 2007. Binary classification using parallel genetic algorithm. Proceedings of the 2007 IEEE Congress on Evolutionary Computation, 1281- 1287.
  35. Zhou X., Wang H., et al., 2006. Biomarker discovery for risk stratification of cardiovascular events using an improved genetic algorithm. Proceedings of Life Science Systems and Applications Workshop, 42-44.
Download


Paper Citation


in Harvard Style

C. To C. and D. Pham T. (2011). COMPLEXITY ANALYSIS OF MASS SPECTROMETRY DATA FOR DISEASE CLASSIFICATION USING GA-BASED MULTISCALE ENTROPY . In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2011) ISBN 978-989-8425-35-5, pages 5-14. DOI: 10.5220/0003119800050014


in Bibtex Style

@conference{biosignals11,
author={Cuong C. To and Tuan D. Pham},
title={COMPLEXITY ANALYSIS OF MASS SPECTROMETRY DATA FOR DISEASE CLASSIFICATION USING GA-BASED MULTISCALE ENTROPY},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2011)},
year={2011},
pages={5-14},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003119800050014},
isbn={978-989-8425-35-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2011)
TI - COMPLEXITY ANALYSIS OF MASS SPECTROMETRY DATA FOR DISEASE CLASSIFICATION USING GA-BASED MULTISCALE ENTROPY
SN - 978-989-8425-35-5
AU - C. To C.
AU - D. Pham T.
PY - 2011
SP - 5
EP - 14
DO - 10.5220/0003119800050014