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摘要翻译：
本文的目的是研究帕金森病、亨廷顿病和肌萎缩侧索硬化症三种复杂动力学疾病的临床时间序列数据。为此，首先将所有的时间序列数据嵌入到一个适当维数的向量空间中，然后估计上述疾病的关联维数。结果还与健康对照受试者进行了比较。下一步，通过所谓的0-1检验来研究混沌在这些疾病中的存在性。仿真结果表明，上述疾病均不是混沌的。
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英文标题：
《Time series analysis of Parkinson's disease, Huntington's disease and
Amyotrophic Lateral Sclerosis》
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作者：
Farshad Merrikh-Bayat
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最新提交年份：
2013
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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：Physics 物理学
二级分类：Medical Physics 医学物理学
分类描述：Radiation therapy. Radiation dosimetry. Biomedical imaging modelling. Reconstruction, processing, and analysis. Biomedical system modelling and analysis. Health physics. New imaging or therapy modalities.
放射治疗。辐射剂量学。生物医学成像建模。重建、处理和分析。生物医学系统建模与分析。健康物理学。新的成像或治疗方式。
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英文摘要：
The aim of this paper is to study the (clinical) time-series data of three diseases with complex dynamics: Parkinson's disease, Huntington's disease and Amyotrophic Lateral Sclerosis. For this purpose, first all of the time series data are embedded in a vector space of suitable dimension and then the correlation dimension of the above mentioned diseases is estimated. The results are also compared with healthy control subjects. At the next step, existence of chaos in these diseases is investigated by means of the so-called 0-1 test. The simulations show that none of the above mentioned diseases are chaotic.
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PDF链接：
https://arxiv.org/pdf/1401.0697
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