Department of Electrical and Computer Engineering, Ryerson University, Toronto, Canada.
J Neural Eng. 2012 Aug;9(4):046004. doi: 10.1088/1741-2560/9/4/046004. Epub 2012 Jun 25.
The onset of a neurological disorder, such as amyotrophic lateral sclerosis (ALS), is so subtle that the symptoms are often overlooked, thereby ruling out the option of early detection of the abnormality. In the case of ALS, over 75% of the affected individuals often experience awkwardness when using their limbs, which alters their gait, i.e. stride and swing intervals. The aim of this work is to suitably represent the non-stationary characteristics of gait (fluctuations in stride and swing intervals) in order to facilitate discrimination between normal and ALS subjects. We define a simple-yet-representative feature vector space by exploiting the ambiguity domain (AD) to achieve efficient classification between healthy and pathological gait stride interval. The stride-to-stride fluctuations and the swing intervals of 16 healthy control and 13 ALS-affected subjects were analyzed. Three features that are representative of the gait signal characteristics were extracted from the AD-space and are fed to linear discriminant analysis and neural network classifiers, respectively. Overall, maximum accuracies of 89.2% (LDA) and 100% (NN) were obtained in classifying the ALS gait.
神经系统疾病（如肌萎缩侧索硬化症，ALS）的发作非常微妙，以至于症状常常被忽视，从而排除了早期发现异常的可能性。在 ALS 的情况下，超过 75%的受影响个体在使用四肢时经常会感到不自在，这会改变他们的步态，即步幅和摆动间隔。本工作的目的是适当地表示步态的非平稳特征（步幅和摆动间隔的波动），以便于区分正常和 ALS 个体。我们通过利用歧义域（AD）来定义一个简单但具有代表性的特征向量空间，以实现健康和病理步态步幅间隔的有效分类。分析了 16 名健康对照和 13 名 ALS 受影响个体的步幅到步幅波动和摆动间隔。从 AD 空间提取了三个代表步态信号特征的特征，并分别将其输入到线性判别分析和神经网络分类器中。总体而言，在分类 ALS 步态时，线性判别分析（LDA）和神经网络（NN）的最高准确率分别达到了 89.2%和 100%。
