Department of Electrical and Computer Engineering, Ryerson University, 350 Victoria Street, Toronto, ON, M5B 2K3, Canada.
Med Biol Eng Comput. 2009 Nov;47(11):1165-71. doi: 10.1007/s11517-009-0527-z. Epub 2009 Aug 26.
Deterioration of motor neurons due to amyotrophic lateral sclerosis (ALS) would affect the strides from one gait cycle to the next. Computer-assisted techniques are useful for gait analysis, and also have high potential in quantitatively monitoring the pathological progression. In this paper, we applied the signal turns count method to measure the fluctuations in the swing-interval time series recorded from 16 healthy control subjects and 13 patients with ALS. The swing-interval turns count (SWITC) parameter derived with the threshold of 0.06 s presented a significant difference (p < 0.001) between the healthy control subjects and ALS patients. Besides the SWITC, we also computed the averaged stride interval (ASI), which is usually longer in the patient with ALS (p < 0.0001), to characterize the gait patterns of ALS patients. In the pattern classification experiments, the Fisher's linear discriminant analysis (FLDA) and the least squares support vector machine (LS-SVM), both input with the SWITC and ASI features, were evaluated using the leave-one-out cross-validation method. The results showed that the LS-SVM with sigmoid kernels was able to provide a classification accurate rate of 89.66% and an area of 0.9629 under the receiver operating characteristic (ROC) curve, which were superior to those obtained with the linear classifier in the form of FLDA.
由于肌萎缩性侧索硬化症（ALS），运动神经元的退化会影响从一个步态周期到下一个步态周期的步幅。计算机辅助技术对于步态分析非常有用，并且在定量监测病理进展方面也具有很高的潜力。在本文中，我们应用信号转弯计数方法来测量从 16 名健康对照者和 13 名肌萎缩性侧索硬化症患者记录的摆动间隔时间序列中的波动。用 0.06 s 的阈值得出的摆动间隔转弯计数（SWITC）参数在健康对照者和肌萎缩性侧索硬化症患者之间存在显著差异（p < 0.001）。除了 SWITC，我们还计算了平均步幅间隔（ASI），它在肌萎缩性侧索硬化症患者中通常更长（p < 0.0001），以表征肌萎缩性侧索硬化症患者的步态模式。在模式分类实验中，Fisher 的线性判别分析（FLDA）和最小二乘支持向量机（LS-SVM）都输入了 SWITC 和 ASI 特征，使用留一法交叉验证方法进行评估。结果表明，具有 S 形核的 LS-SVM 能够在接收者操作特征（ROC）曲线下提供 89.66%的分类准确率和 0.9629 的面积，优于以 FLDA 形式的线性分类器的结果。
