中国组织工程研究

中国组织工程研究 ›› 2010, Vol. 14 ›› Issue (13): 2377-2380.doi: 10.3969/j.issn.1673-8225.2010.13.025

• 数字化骨科 digital orthopedics • 上一篇    下一篇

小波包变换分析帕金森病患者足底的压力信号

韩  阳，周  萍，侯园园，李  宁   

  1. 首都医科大学生物医学工程学院，北京市 100069　
  • 出版日期:2010-03-26 发布日期:2010-03-26
  • 通讯作者: 李 宁，副教授，首都医科大学生物医学工程学院，北京市 100069 eduhelp@163.com
  • 作者简介:韩 阳，女，1988年生，江苏省南京市，汉族，首都医科大学在读，主要从事生物医学图像处理研究。 yanghan88@gmail.com
  • 基金资助:

    首都医科大学基础临床基金(2007JL25)及北京市教委“人才强教计划”2005年北京市中青年骨干教师资助项目。

Analysis of gait signal based on wavelet packet decomposition in Parkinson’s disease patients

Han Yang, Zhou Ping, Hou Yuan-yuan, Li Ning   

  1. School of Biomedical Engineering, Capital Medical University, Beijing  100069, China
  • Online:2010-03-26 Published:2010-03-26
  • Contact: Li Ning, Associate professor, School of Biomedical Engineering, Capital Medical University, Beijing 100069, China eduhelp@163.com
  • About author:Han Yang, School of Biomedical Engineering, Capital Medical University, Beijing 100069, China yanghan88@gmail.com
  • Supported by:

    the Basic Clinical Foundation of Capital Medical University, No. 2007JL25*; the Beijing Middle and Youth Key Teacher Foundation of “Talent Teaching Strength Project” of Beijing Municipal Education Committee in 2005*

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514

被引次数

9

摘要：

背景：帕金森病属中枢神经系统的退行性疾病，步态失调是帕金森病3大特征之一。因此对帕金森病患者足底压力的研究较为广泛。以往研究多集中于从力学方面揭示足底压力的分布，而对足底压力信号的研究较少。
目的：观察比较帕金森病患者与正常人足底压力信号的差异。
方法：采集93例帕金森病患者和72名正常对照的足底数据。被试者以其平时自然的步态在水平地面上行走约2 min，足底在垂直方向所受的反作用力(N)以时间序列记录下来。足底的16个传感器以采样100次/s的速度记录，数据中还包括了每只足底8个传感器的记录之和。采样速度为100 Hz，每个被试采样时间为121.171 5 s。利用小波包分解来分析帕金森病患者和对照组步态数据。
结果与结论：通过分解和计算，得到了来自足底不同传感器的信号的熵。D1层分解结果的熵值显示，来自于传感器L1，R1，L2，R2，L6和R6信号的P值明显小于其他传感器的数值，且小于0.05；DD2层分解结果的熵值显示，来自于传感器L1，R1，L6和R6信号的P值明显小于其他传感器的数值，且小于0.05。由统计结果可知，帕金森病患者与正常对照的足底压力信号熵值存在明显差异，通过分析足底压力信号可以辅助医师诊断和治疗帕金森病患者。

关键词: 帕金森病, 小波包, 小波熵, 步态, 足底运动, 压力信号, 数字化神经科技术 

Abstract:

BACKGROUND: Parkinson’s disease (PD) is a degenerative disease of the central nervous system. Disturbed gait is one of 3 features of PD. Therefore, it is widely to study Parkinson's foot pressure. Previous studies have focused on pressure distribution, but the gait signal research is little.
OBJECTIVE: To find the difference of gait signal between PD patients and normal people.
METHODS: Foot data of 93 PD patients and 72 normal controls were collected. Subjects walked for about 2 minutes as normal on the horizontal ground. The counteracting force on the foot sole from vertical direction was recorded. Sixteen transducers on the foot sole were used to record at 100 times/s. The data included the sum of 8 transducers on the foot sole. Sampling speed was 100 Hz. The sampling time of each subject was 121.1715 s. Gait data of PD patients and controls were analyzed using wavelet packet decomposition algorithm.
RESULTS AND CONCLUSION: Entropy of signal of different transducers from foot sole was obtained by decomposition and calculation. D1 layer entropy value showed that P value from transducers L1, R1, L2, R2, L6 and R6 signal was significantly less than other transducers, and less than 0.05. DD2 layer entropy value showed that P value from transducers L1, R1, L6 and R6 signal was significantly less than other transducers, and less than 0.05. Statistical results demonstrated that there are significant differences in signal entropy value in foot sole between PD patients and normal controls. Foot sole pressure signal analysis can help physician diagnosis and PD treatment.

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