|
 
 |
| LETTER TO THE EDITOR |
|
| Year : 2012 | Volume
: 2
| Issue : 2 | Page : 112-113 |
|
Is Stretching and Folding Feature of Chaotic Trajectories Useful in Adaptive Local Projection?
Sajad Jafari1, SM Reza Hashemi Golpayegani1, Amir H Jafari2
1 Department of Bioelectric Engineering, Biomedical Engineering Faculty, Amirkabir University of Technology, Tehran, Iran
2 Department of Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran
| Date of Web Publication | 20-Sep-2019 |
Correspondence Address:
Sajad Jafari
Department of Bioelectric Engineering, Biomedical Engineering Faculty, Amirkabir University of Technology, Tehran
Iran
 Source of Support: None, Conflict of Interest: None
DOI: 10.4103/2228-7477.110335
How to cite this article:
Jafari S, Reza Hashemi Golpayegani S M, Jafari AH. Is Stretching and Folding Feature of Chaotic Trajectories Useful in Adaptive Local Projection?. J Med Signals Sens 2012;2:112-3 |
How to cite this URL:
Jafari S, Reza Hashemi Golpayegani S M, Jafari AH. Is Stretching and Folding Feature of Chaotic Trajectories Useful in Adaptive Local Projection?. J Med Signals Sens [serial online] 2012 [cited 2023 Jun 4];2:112-3. Available from: https://www.jmssjournal.net/text.asp?2012/2/2/112/110335 |
Sir,
Chaotic behavior is a feature associated with complex and interacted systems. Many natural and unnatural systems in various branches of science (such as biology, economics, etc.) exhibit chaotic behavior and the study of chaotic systems and signals has progressed in the recent decades. [1] Chaotic time series have a significant role in identification of their generating systems. It has been claimed that in medical science many signals like brain signals (both microscopic and macroscopic ones), [2],[3] cardiac signals (e.g., ECG and HRV [1],[4] ), respiratory sounds, [5] etc. have chaotic properties. Owing to the effect of measurement instruments and the environment, all experimental data are mixed with noise to some extent. This fact is often undesirable. In other words, noise is an unwanted part of data. [1],[6]
Different methods for removing noise from chaotic signals have been introduced. One of the best methods is the Local Projection approach, [1] The local projection approach projects the chaotic data in a neighborhood onto a certain hyperplane. Selection of neighborhood radius, which is mainly determined by the way of experience or trial-and-error methods, has a direct impact on its performance. There are a few works on choosing the neighborhood radius adaptively. [7],[8],[9]
We believe that the use of this method can improve the local projection approach efficiency. In addition, this idea (using Stretching and folding feature and the way it should be measured) could also be used in other areas of chaotic signal processing.[11]
| References | |  |
| 1. | Kantz H, Schreiber T. Nonlinear Time Series Analysis. Cambridge, UK: Cambridge University Press; 1997.  |
| 2. | Korn H, Faure P. Is there chaos in the brain? II. Experimental evidence and related models. C R Biol 2003;326:787-840. |
| 3. | Gong YF, Ren W, Shi XZ, Xu JX, Hu SJ. Recovering strange attractors from noisy interspike intervals of neuronal firings. Phys Lett A 1999;258:253-62. |
| 4. | Signorini MG, Marchetti F, Cirigioni A, Cerutti S. Nonlinear noise reduction for the analysis oh heart rate variability signals in normal and heart transplanted subjects. Proceedings - 19th International Conference, Chicago, IL, USA; 1997. |
| 5. | Ahlstrom C, Johansson A, Hult P, Ask P. Chaotic dynamics of respiratory sounds. Chaos Solitons Fractals 2006;29:1054-62. |
| 6. | Kostelich EJ, Schreiber T. Noise reduction in chaotic time-series data: A survey of common methods. Phys Rev E 1993;48:1752-63. |
| 7. | Matassini L, Kantz H. Optimizing of recurrence plots for noise reduction. Phys Rev E 2002;65:1-6. |
| 8. | Mingda W, Laibin Z, Wei L, Lixiang D. Research on the noise reduction for chaotic signals based on the adaptive local projection approach. 2010 International Conference on Measuring Technology and Mechatronics Automation, 2010 |
| 9. | Kern A, Blank D, Stoop R. Projective noise reduction with dynamic neighborhood selection. ISCAS 2000 - IEEE International Symposium on Circuits and Systems. Geneva, Switzerland, 2000. |
| 10. | Hilborn RC. Chaos and nonlinear dynamics: An introduction for scientists and engineers. 2nd ed. USA: Oxford University Press; 2001. |
| 11. | Kline M. Calculus: An Intuitive and Physical Approach. New York, NY, USA: Dover Publications; 1998. |
|
Search Pubmed for