摘要翻译：
在生物时间序列分析中，状态空间是研究具有确定性性质的系统的框架。然而，生理实验通常捕获一个可观察的，或者，换句话说，一系列标量测量，表征所研究的生理系统的时间反应；构成系统任何时刻状态的动态变量是不可用的。因此，只有从获取的观测数据中重建状态向量，才能模拟底层系统的不同状态。这就是所谓的状态空间重构，在现实世界的信号中称为相空间，目前只能用延迟的方法令人满意地解决。每个状态向量由m个分量组成，这些分量是从延迟时间t的连续观测中提取的。状态向量所描述的几何结构的形态及其性质取决于所选择的参数t和M。所研究系统的真实动力学取决于参数t和M的正确确定。只有这样，才能推导出具有真正物理意义的特征，揭示可靠地识别生理系统动态复杂性的方面。作为一个案例研究，本研究中提出的生物信号是光电体积描记(PPG)信号。我们发现，对于所有被分析的对象来说，m是5，而t取决于它评估的时间间隔。H\'Enon图和Lorenz流被用来促进对应用技术的更直观的理解。
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英文标题：
《Phase space reconstruction from a biological time series. A
  PhotoPlethysmoGraphic signal a case study》
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作者：
J. de Pedro-Carracedo, A.M. Ugena, and A.P. Gonzalez-Marcos
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最新提交年份：
2019
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分类信息：

一级分类：Quantitative Biology        数量生物学
二级分类：Other Quantitative Biology        其他定量生物学
分类描述：Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
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一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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一级分类：Physics        物理学
二级分类：Adaptation and Self-Organizing Systems        自适应和自组织系统
分类描述：Adaptation, self-organizing systems, statistical physics, fluctuating systems, stochastic processes, interacting particle systems, machine learning
自适应，自组织系统，统计物理，波动系统，随机过程，相互作用粒子系统，机器学习
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英文摘要：
  In the analysis of biological time series, the state space comprises a framework for the study of systems with presumably deterministic properties. However, a physiological experiment typically captures an observable, or, in other words, a series of scalar measurements that characterize the temporal response of the physiological system under study; the dynamic variables that make up the state of the system at any time are not available. Therefore, only from the acquired observations should state vectors reconstructed to emulate the different states of the underlying system. It is what is known as the reconstruction of the state space, called phase space in real-world signals, for now only satisfactorily resolved using the method of delays. Each state vector consists of m components, extracted from successive observations delayed a time t. The morphology of the geometric structure described by the state vectors, as well as their properties, depends on the chosen parameters t and m. The real dynamics of the system under study is subject to the correct determination of the parameters t and m. Only in this way can be deduced characteristics with true physical meaning, revealing aspects that reliably identify the dynamic complexity of the physiological system. The biological signal presented in this work, as a case study, is the PhotoPlethysmoGraphic (PPG) signal. We find that m is five for all the subjects analyzed and that t depends on the time interval in which it evaluates. The H\'enon map and the Lorenz flow are used to facilitate a more intuitive understanding of applied techniques.
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PDF链接：
https://arxiv.org/pdf/1910.05410