Analysis of altered gait cycle duration in amyotrophic lateral sclerosis based on nonparametric probability density function estimation

• Published in: Medical engineering & physics Vol. 33; no. 3; pp. 347 - 355
• Main Authors: Wu, Yunfeng, Shi, Lei
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.04.2011; Elsevier
• Subjects: Amyotrophic lateral sclerosis; Amyotrophic Lateral Sclerosis - physiopathology; Biological and medical sciences; Case-Control Studies; Central nervous system; Cerebrospinal fluid. Meninges. Spinal cord; Classification; Degenerative and inherited degenerative diseases of the nervous system. Leukodystrophies. Prion diseases; Female; Foot; gait; Gait - physiology; Gait analysis; Humans; Kernels; Locomotion; Male; Medical sciences; Middle Aged; Movement disorders; Nervous system (semeiology, syndromes); Neurology; Nonlinear Dynamics; Parzen window; Probability density function; Probability Theory; Radiology; Rhythms; ROC Curve; Statistics; Statistics, Nonparametric; Support vector machine; Probability density function; Amyotrophic lateral sclerosis; Parzen window; Support vector machine; Gait analysis; Movement disorders; Human; Nervous system diseases; Modeling; Abnormal movement; Locomotion; Walking; Statistical model; Central nervous system disease; Body movement; Degenerative disease; Spinal cord disease; Biomedical engineering
• ISSN: 1350-4533; 1873-4030; 1873-4030
• DOI: 10.1016/j.medengphy.2010.10.023

Human locomotion is regulated by the central nervous system (CNS). The neurophysiological changes in the CNS due to amyotrophic lateral sclerosis (ALS) may cause altered gait cycle duration (stride interval) or other gait rhythm. This article used a statistical method to analyze the altered stride interval in patients with ALS. We first estimated the probability density functions (PDFs) of stride interval from the outlier-processed gait rhythm time series, by using the nonparametric Parzen-window approach. Based on the PDFs estimated, the mean of the left-foot stride interval and the modified Kullback–Leibler divergence (MKLD) can be computed to serve as dominant features. In the classification experiments, the least squares support vector machine (LS-SVM) with Gaussian kernels was applied to distinguish the stride patterns in ALS patients. According to the results obtained with the stride interval time series recorded from 16 healthy control subjects and 13 patients with ALS, the key findings of the present study are summarized as follows. (1) It is observed that the mean of stride interval computed based on the PDF for the left foot is correlated with that for the right foot in patients with ALS. (2) The MKLD parameter of the gait in ALS is significantly different from that in healthy controls. (3) The diagnostic performance of the nonlinear LS-SVM, evaluated by the leave-one-out cross-validation method, is superior to that obtained by the linear discriminant analysis. The LS-SVM can effectively separate the stride patterns between the groups of healthy controls and ALS patients with an overall accurate rate of 82.8% and an area of 0.869 under the receiver operating characteristic curve.