生理时间序列复杂波动的自适应数据分析
ADAPTIVE DATA ANALYSIS OF COMPLEX FLUCTUATIONS IN PHYSIOLOGIC TIME SERIES.
作者信息
Peng C-K, Costa Madalena, Goldberger Ary L
机构信息
Margret & H.A. Rey Institute of Nonlinear Dynamics in Physiology and Medicine Division of Interdisciplinary Medicine and Biotechnology Beth Israel Deaconess Medical Center Harvard Medical School 330 Brookline Ave., Boston, MA 02215, USA.
出版信息
Adv Adapt Data Anal. 2009 Jan 1;1(1):61-70. doi: 10.1142/S1793536909000035.
Abstract
We introduce a generic framework of dynamical complexity to understand and quantify fluctuations of physiologic time series. In particular, we discuss the importance of applying adaptive data analysis techniques, such as the empirical mode decomposition algorithm, to address the challenges of nonlinearity and nonstationarity that are typically exhibited in biological fluctuations.
摘要
我们引入了一个动态复杂性的通用框架，以理解和量化生理时间序列的波动。特别是，我们讨论了应用自适应数据分析技术（如经验模式分解算法）来应对生物波动中典型表现出的非线性和非平稳性挑战的重要性。
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本文引用的文献
1
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Physica A. 2003 Dec 1;330(1-2):53-60. doi: 10.1016/j.physa.2003.08.022. Epub 2003 Sep 21.
2
Altered Phase Interactions between Spontaneous Blood Pressure and Flow Fluctuations in Type 2 Diabetes Mellitus: Nonlinear Assessment of Cerebral Autoregulation.2型糖尿病患者自发性血压与血流波动之间的相位相互作用改变：脑自动调节的非线性评估
Physica A. 2008 Apr 1;387(10):2279-2292. doi: 10.1016/j.physa.2007.11.052.
3
Nonlinear assessment of cerebral autoregulation from spontaneous blood pressure and cerebral blood flow fluctuations.基于自发性血压和脑血流波动对脑自动调节功能的非线性评估
Cardiovasc Eng. 2008 Mar;8(1):60-71. doi: 10.1007/s10558-007-9045-5.
4
On the trend, detrending, and variability of nonlinear and nonstationary time series.关于非线性和非平稳时间序列的趋势、去趋势以及变异性
Proc Natl Acad Sci U S A. 2007 Sep 18;104(38):14889-94. doi: 10.1073/pnas.0701020104. Epub 2007 Sep 10.
5
Noise and poise: Enhancement of postural complexity in the elderly with a stochastic-resonance-based therapy.噪声与平衡：基于随机共振疗法增强老年人的姿势复杂性
Europhys Lett. 2007 Mar;77:68008. doi: 10.1209/0295-5075/77/68008.
6
Broken asymmetry of the human heartbeat: loss of time irreversibility in aging and disease.人体心跳的破碎不对称性：衰老和疾病中时间不可逆性的丧失。
Phys Rev Lett. 2005 Nov 4;95(19):198102. doi: 10.1103/PhysRevLett.95.198102.
7
Multiscale entropy analysis of biological signals.生物信号的多尺度熵分析
Phys Rev E Stat Nonlin Soft Matter Phys. 2005 Feb;71(2 Pt 1):021906. doi: 10.1103/PhysRevE.71.021906. Epub 2005 Feb 18.
8
Multimodal pressure-flow method to assess dynamics of cerebral autoregulation in stroke and hypertension.多模态压力-血流方法评估中风和高血压患者脑自动调节功能的动态变化
Biomed Eng Online. 2004 Oct 25;3(1):39. doi: 10.1186/1475-925X-3-39.
9
Entropy estimation of symbol sequences.符号序列的熵估计
Chaos. 1996 Sep;6(3):414-427. doi: 10.1063/1.166191.
10
What is complexity?什么是复杂性？
Bioessays. 2002 Dec;24(12):1085-94. doi: 10.1002/bies.10192.
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