采用步态黑箱模型参数作为判别帕金森病与健康状态的标准

Masood Banaie, M. Pooyan, Yashar Sarbaz, S. Gharibzadeh, F. Towhidkhah
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引用次数: 1

摘要

帕金森病患者大多表现出一些运动障碍。步态障碍是其中最主要的一种。在这项研究中,我们关注步态,并提出了一个黑箱模型来产生步幅时间序列。我们试图在正常人和PD人的混沌关系的基础上提出一个模型。由于步态是半周期的,具有分形特性,我们采用正弦圆映射关系。可以假设这种关系的参数与BG结构之间有相似之处。因此,这种关系可以解释BG的复杂行为和复杂结构。所提出的模型可以全面模拟BG的行为。Ω模型的参数在模型响应中起关键作用。这是决定模型是代表一个正常人还是一个PD患者的主要因素。我们的统计检验表明,正常与PD患者的Ω有显著性差异。我们认为Ω可以作为一个参数来区分正常人和PD患者。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using a parameter of black box model for gait as a criterion to differentiate between parkinson disease & healthy states
Parkinsonian patients mostly show some movement disorders. Gait disorder is one of the cardinal ones of them. In this study, we have paid attention to gait and presented a black box model for producing stride time series. We tried to present a model on the basis of a chaotic relation for normal and PD persons. Since gait is semi-periodic and has fractal properties, we used sine circle map relation. It is possible to suppose similarities between the parameters of this relation and BG structure. Therefore, this relation can explain the complex behaviours and complex structure of BG. The presented model can simulate globally the BG behaviour. Ω parameter of the model has a key role in the model response. It is the main factor which determines that the model is representing a normal person or a PD patient. Our statistical tests show that there is significant difference between the Ω of normal and PD patients. We conclude that Ω can be introduced as a parameter to distinguish normal and PD persons.
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