序数符号分析及其在生物医学记录中的应用。
Ordinal symbolic analysis and its application to biomedical recordings.
作者信息
Amigó José M, Keller Karsten, Unakafova Valentina A
机构信息
Centro de Investigación Operativa, Universidad Miguel Hernández, Avda. de la Universidad s/n, 03202 Elche, Spain
Institut für Mathematik, Universität zu Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany.
出版信息
Philos Trans A Math Phys Eng Sci. 2015 Feb 13;373(2034). doi: 10.1098/rsta.2014.0091.
Abstract
Ordinal symbolic analysis opens an interesting and powerful perspective on time-series analysis. Here, we review this relatively new approach and highlight its relation to symbolic dynamics and representations. Our exposition reaches from the general ideas up to recent developments, with special emphasis on its applications to biomedical recordings. The latter will be illustrated with epilepsy data.
摘要
序数符号分析为时间序列分析开启了一个有趣且强大的视角。在此，我们回顾这种相对较新的方法，并强调其与符号动力学及表示法的关系。我们的阐述从一般概念到近期进展，特别着重于其在生物医学记录中的应用。后者将通过癫痫数据进行说明。
相似文献
1
Ordinal symbolic analysis and its application to biomedical recordings.序数符号分析及其在生物医学记录中的应用。
Philos Trans A Math Phys Eng Sci. 2015 Feb 13;373(2034). doi: 10.1098/rsta.2014.0091.
2
Transcripts: an algebraic approach to coupled time series.语料库：耦合时间序列的代数方法。
Chaos. 2012 Mar;22(1):013105. doi: 10.1063/1.3673238.
3
Is human atrial fibrillation stochastic or deterministic?-Insights from missing ordinal patterns and causal entropy-complexity plane analysis.人类心房颤动是随机的还是确定性的？——来自缺失序数模式和因果熵-复杂度平面分析的见解。
Chaos. 2018 Jun;28(6):063130. doi: 10.1063/1.5023588.
4
Differentiating resting brain states using ordinal symbolic analysis.使用序数符号分析区分静息脑状态。
Chaos. 2018 Oct;28(10):106307. doi: 10.1063/1.5036959.
5
[Symbolic dynamics analysis of epileptic EEG signals of the rat].[大鼠癫痫脑电信号的符号动力学分析]
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2012 Aug;29(4):760-3.
6
[The sample entropy and its application in EEG based epilepsy detection].[样本熵及其在基于脑电图的癫痫检测中的应用]
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2007 Feb;24(1):200-5.
7
Ordinal pattern based similarity analysis for EEG recordings.基于序贯模式的脑电记录相似性分析。
Clin Neurophysiol. 2010 May;121(5):694-703. doi: 10.1016/j.clinph.2009.12.030. Epub 2010 Jan 22.
8
Characterization of sample entropy in the context of biomedical signal analysis.生物医学信号分析背景下样本熵的特征描述。
Annu Int Conf IEEE Eng Med Biol Soc. 2007;2007:5943-6. doi: 10.1109/IEMBS.2007.4353701.
9
Optimal instruments and models for noisy chaos.用于噪声混沌的最优仪器和模型。
Chaos. 2007 Dec;17(4):043127. doi: 10.1063/1.2818152.
10
Algorithmics, Possibilities and Limits of Ordinal Pattern Based Entropies.基于序数模式的熵的算法、可能性与局限性
Entropy (Basel). 2019 May 29;21(6):547. doi: 10.3390/e21060547.
引用本文的文献
1
Nearest neighbor permutation entropy detects phase transitions in complex high-pressure systems.最近邻排列熵检测复杂高压系统中的相变。
Sci Rep. 2025 Jul 1;15(1):22410. doi: 10.1038/s41598-025-06016-7.
2
Identifying Ordinal Similarities at Different Temporal Scales.识别不同时间尺度上的序数相似性。
Entropy (Basel). 2024 Nov 24;26(12):1016. doi: 10.3390/e26121016.
3
Stationarity assessment of resting state condition via permutation entropy on EEG recordings.基于脑电图记录通过排列熵对静息状态进行平稳性评估。
Sci Rep. 2025 Jan 3;15(1):698. doi: 10.1038/s41598-024-82089-0.
4
Revisiting the Characterization of Resting Brain Dynamics with the Permutation Jensen-Shannon Distance.用排列詹森 - 香农距离重新审视静息脑动力学的特征
Entropy (Basel). 2024 May 20;26(5):432. doi: 10.3390/e26050432.
5
Empirical phenotyping in coupled patient+care systems: Generating low-dimensional categories for hypothesis-driven investigation of mechanically-ventilated patients.耦合患者+护理系统中的经验性表型分析：为机械通气患者的假设驱动研究生成低维类别。
medRxiv. 2025 Jan 16:2023.12.14.23299978. doi: 10.1101/2023.12.14.23299978.
6
On the Genuine Relevance of the Data-Driven Signal Decomposition-Based Multiscale Permutation Entropy.基于数据驱动信号分解的多尺度排列熵的真正相关性
Entropy (Basel). 2022 Sep 23;24(10):1343. doi: 10.3390/e24101343.
7
On Rényi Permutation Entropy.关于雷尼排列熵
Entropy (Basel). 2021 Dec 24;24(1):37. doi: 10.3390/e24010037.
8
Extracting Robust Biomarkers From Multichannel EEG Time Series Using Nonlinear Dimensionality Reduction Applied to Ordinal Pattern Statistics and Spectral Quantities.利用应用于序数模式统计和频谱量的非线性降维从多通道脑电图时间序列中提取稳健的生物标志物。
Front Physiol. 2021 Feb 1;11:614565. doi: 10.3389/fphys.2020.614565. eCollection 2020.
9
Time Series Complexities and Their Relationship to Forecasting Performance.时间序列复杂性及其与预测性能的关系。
Entropy (Basel). 2020 Jan 10;22(1):89. doi: 10.3390/e22010089.
10
Ordinal Patterns in Heartbeat Time Series: An Approach Using Multiscale Analysis.心跳时间序列中的序数模式：一种使用多尺度分析的方法。
Entropy (Basel). 2019 Jun 12;21(6):583. doi: 10.3390/e21060583.
本文引用的文献
1
Change-Point Detection Using the Conditional Entropy of Ordinal Patterns.使用有序模式的条件熵进行变点检测。
Entropy (Basel). 2018 Sep 14;20(9):709. doi: 10.3390/e20090709.
2
Information directionality in coupled time series using transcripts.使用转录本的耦合时间序列中的信息方向性。
Phys Rev E Stat Nonlin Soft Matter Phys. 2013 Aug;88(2):022911. doi: 10.1103/PhysRevE.88.022911. Epub 2013 Aug 12.
3
Modified permutation-entropy analysis of heartbeat dynamics.心跳动力学的修正排列熵分析
Phys Rev E Stat Nonlin Soft Matter Phys. 2012 Feb;85(2 Pt 1):021906. doi: 10.1103/PhysRevE.85.021906. Epub 2012 Feb 10.
4
Transcripts: an algebraic approach to coupled time series.语料库：耦合时间序列的代数方法。
Chaos. 2012 Mar;22(1):013105. doi: 10.1063/1.3673238.
5
Entropy measures for discrimination of 'awake' Vs 'anaesthetized' state in recovery from general anesthesia.用于区分全身麻醉苏醒期“清醒”与“麻醉”状态的熵测度
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:2598-601. doi: 10.1109/IEMBS.2011.6090717.
6
Characterization of early partial seizure onset: frequency, complexity and entropy.早期部分性发作起始的特征：频率、复杂度和熵。
Clin Neurophysiol. 2012 Apr;123(4):658-69. doi: 10.1016/j.clinph.2011.08.003. Epub 2011 Aug 26.
7
Forbidden ordinal patterns of periictal intracranial EEG indicate deterministic dynamics in human epileptic seizures.禁则的颅内 EEG 癫痫发作期模式表明人类癫痫发作中的确定性动力学。
Epilepsia. 2011 Oct;52(10):1771-80. doi: 10.1111/j.1528-1167.2011.03202.x. Epub 2011 Aug 12.
8
Momentary information transfer as a coupling measure of time series.作为时间序列耦合度量的瞬时信息传递
Phys Rev E Stat Nonlin Soft Matter Phys. 2011 May;83(5 Pt 1):051122. doi: 10.1103/PhysRevE.83.051122. Epub 2011 May 19.
9
Performance of anesthetic depth indexes in rabbits under propofol anesthesia: prediction probabilities and concentration-effect relations.兔异丙酚麻醉下麻醉深度指数的性能：预测概率和浓度-效应关系。
Anesthesiology. 2011 Aug;115(2):303-14. doi: 10.1097/ALN.0b013e318222ac02.
10
Classifying cardiac biosignals using ordinal pattern statistics and symbolic dynamics.基于序贯模式统计和符号动力学对心脏生物信号进行分类。
Comput Biol Med. 2012 Mar;42(3):319-27. doi: 10.1016/j.compbiomed.2011.03.017. Epub 2011 Apr 20.
Zotero 插件