For the purpose of realizing an intelligent and highly accurate diagnosis system for neuro-degenerative diseases (NDD), such as amyotrophic lateral sclerosis (ALS), Parkinson's disease (PD) and Huntington's disease (HD), the present study investigated the classification capability of different gait statistical features extracted from gait rhythm signals. Nine statistical measures, including several seldom-used variability measures for these signals, were calculated for each time series. Next, after an evaluation of four popular machine learning methods, the optimal feature subset was generated with a hill-climbing feature selection method. Experiments were performed on a data set with 16 healthy control (CO) subjects, 13 ALS, 15 PD and 20 HD patients. When evaluated with the leave-one-out cross-validation (LOOCV) method, the highest accuracy rate for discriminating between groups of NDD patients and healthy control subjects was 96.83%. The best classification accuracy (100%) was obtained with two subtype binary classifiers (PD vs. CO and HD vs. CO). 
Classification of gait rhythm signals between patients with neuro-degenerative diseases and normal subjects: Experiments with statistical features and different classification models