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Characterization of chaotic dynamics in the human menstrual cycle

BACKGROUND: The human menstrual cycle is known to exhibit a significant amount of unexplained variability. This variation is typically dismissed as random fluctuations in an otherwise periodic and predictable system. Given the many delayed nonlinear feedbacks in the multiple levels of the reproducti...

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Autores principales: Derry, GN, Derry, PS
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2959058/
https://www.ncbi.nlm.nih.gov/pubmed/20923559
http://dx.doi.org/10.1186/1753-4631-4-5
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author Derry, GN
Derry, PS
author_facet Derry, GN
Derry, PS
author_sort Derry, GN
collection PubMed
description BACKGROUND: The human menstrual cycle is known to exhibit a significant amount of unexplained variability. This variation is typically dismissed as random fluctuations in an otherwise periodic and predictable system. Given the many delayed nonlinear feedbacks in the multiple levels of the reproductive endocrine system, however, the menstrual cycle can properly be construed as the output of a nonlinear dynamical system, and such a system has the possibility of being in a chaotic trajectory. We hypothesize that this is in fact the case and that it accounts for the observed variability. RESULTS: Here, we test this hypothesis by performing time series analyses on data for 7749 menstrual cycles from 40 women in the 20-40 year age range, using the database maintained by the Tremin Research Program on Women's Health. Both raw menstrual cycle length data and a formal time series constructed from this data are utilized in these analyses. Employing phase space reconstruction techniques with a maximum embedding dimension of 12, we find appropriate scaling behavior in the correlation sums for these data, indicating low dimensional deterministic dynamics. A correlation dimension of D(c )≈ 5.2 is measured in the scaling regime. This result is confirmed by recalculation using the Takens estimator and by surrogate data tests. We interpret this result as an approximation to the fractal dimension of a strange attractor governing chaotic dynamics in the menstrual cycle. We also use the time series to calculate the correlation entropy (K(2 )≈ 0.008/τ) and the maximal Lyapunov exponent (λ ≈ 0.005/τ) for the system, where τ is the sampling time of the series. CONCLUSIONS: Taken collectively, these results constitute significant evidence that the menstrual cycle is the result of chaos in a nonlinear dynamical system. This view of the menstrual cycle has potential implications for clinical practice, modelling of the endocrine system, and the interpretation of the perimenopausal transition.
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spelling pubmed-29590582010-10-25 Characterization of chaotic dynamics in the human menstrual cycle Derry, GN Derry, PS Nonlinear Biomed Phys Debate BACKGROUND: The human menstrual cycle is known to exhibit a significant amount of unexplained variability. This variation is typically dismissed as random fluctuations in an otherwise periodic and predictable system. Given the many delayed nonlinear feedbacks in the multiple levels of the reproductive endocrine system, however, the menstrual cycle can properly be construed as the output of a nonlinear dynamical system, and such a system has the possibility of being in a chaotic trajectory. We hypothesize that this is in fact the case and that it accounts for the observed variability. RESULTS: Here, we test this hypothesis by performing time series analyses on data for 7749 menstrual cycles from 40 women in the 20-40 year age range, using the database maintained by the Tremin Research Program on Women's Health. Both raw menstrual cycle length data and a formal time series constructed from this data are utilized in these analyses. Employing phase space reconstruction techniques with a maximum embedding dimension of 12, we find appropriate scaling behavior in the correlation sums for these data, indicating low dimensional deterministic dynamics. A correlation dimension of D(c )≈ 5.2 is measured in the scaling regime. This result is confirmed by recalculation using the Takens estimator and by surrogate data tests. We interpret this result as an approximation to the fractal dimension of a strange attractor governing chaotic dynamics in the menstrual cycle. We also use the time series to calculate the correlation entropy (K(2 )≈ 0.008/τ) and the maximal Lyapunov exponent (λ ≈ 0.005/τ) for the system, where τ is the sampling time of the series. CONCLUSIONS: Taken collectively, these results constitute significant evidence that the menstrual cycle is the result of chaos in a nonlinear dynamical system. This view of the menstrual cycle has potential implications for clinical practice, modelling of the endocrine system, and the interpretation of the perimenopausal transition. BioMed Central 2010-10-05 /pmc/articles/PMC2959058/ /pubmed/20923559 http://dx.doi.org/10.1186/1753-4631-4-5 Text en Copyright ©2010 Derry and Derry; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Debate
Derry, GN
Derry, PS
Characterization of chaotic dynamics in the human menstrual cycle
title Characterization of chaotic dynamics in the human menstrual cycle
title_full Characterization of chaotic dynamics in the human menstrual cycle
title_fullStr Characterization of chaotic dynamics in the human menstrual cycle
title_full_unstemmed Characterization of chaotic dynamics in the human menstrual cycle
title_short Characterization of chaotic dynamics in the human menstrual cycle
title_sort characterization of chaotic dynamics in the human menstrual cycle
topic Debate
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2959058/
https://www.ncbi.nlm.nih.gov/pubmed/20923559
http://dx.doi.org/10.1186/1753-4631-4-5
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