Aziz Wajid, Arif Muhammad
Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences (PIEAS), Nilore, Islamabad, Pakistan.
Eur J Appl Physiol. 2006 Sep;98(1):30-40. doi: 10.1007/s00421-006-0226-5. Epub 2006 Jul 14.
The stride interval of human gait fluctuates in complex fashion. It reflects the rhythm of the locomotor system. The temporal fluctuations in the stride interval provide us a non-invasive technique to evaluate the effects of neurological impairments on gait and its changes with age and disease. In this paper, we have used threshold dependent symbolic entropy, which is based on symbolic nonlinear time series analysis to study complexity of gait of control and neurodegenerative disease subjects. Symbolic entropy characterizes quantitatively the complexity even in time series having relatively few data points. We have calculated normalized corrected Shannon entropy (NCSE) of symbolic sequences extracted from stride interval time series. This measure of complexity showed significant difference between control and neurodegenerative disease subjects for a certain range of thresholds. We have also investigated complexity of physiological signal and randomized noisy data. In the study, we have found that the complexity of physiological signal was higher than that of random signals at short threshold values.
人类步态的步幅间隔以复杂的方式波动。它反映了运动系统的节律。步幅间隔的时间波动为我们提供了一种非侵入性技术，用于评估神经损伤对步态的影响及其随年龄和疾病的变化。在本文中，我们使用了基于符号非线性时间序列分析的阈值依赖符号熵来研究对照组和神经退行性疾病患者步态的复杂性。符号熵即使在数据点相对较少的时间序列中也能定量地表征复杂性。我们计算了从步幅间隔时间序列中提取的符号序列的归一化校正香农熵（NCSE）。对于一定范围的阈值，这种复杂性度量在对照组和神经退行性疾病患者之间显示出显著差异。我们还研究了生理信号和随机噪声数据的复杂性。在这项研究中，我们发现在短阈值下生理信号的复杂性高于随机信号。
