Comparison of the performance of two fusion algorithms at the feature and signal level in separating the gait signal of amyotrophic lateral sclerosis patients from healthy individuals

Amyotrophic lateral sclerosis (ALS) is a neuromuscular disease, the most common disease of motor neurons. Since one of the most important early symptoms of the disease is the presence of movement disorders, the study of gait disorders has been the focus of many researchers. The aim of this study was to provide a suitable algorithm for the diagnosis of ALS. The data available in the Physionet database were used. They were recorded from 13 ALS patients and 16 healthy individuals. In this study, two methods of fusion have been employed to combine the information of the right and left foot signals, before feature extraction (signal-level fusion) and after feature extraction (feature-level fusion). We utilized the nonlinear features to characterize the gait signals of patients and healthy individuals, which includes: Energy-logarithm entropy, Shannon entropy, Higuchi fractal, and Katz fractal. Then, by performing the Wilcoxon statistical test on the extracted features, we tried to find significant differences between the groups. A support vector machine was used to separate ALS subjects from the normal group. The suggested algorithm has the ability to diagnose ALS with an average accuracy of 87%. The highest classification accuracy was obtained using the Katz feature, which is 100%. The proposed system based on fusion algorithms not only reduces the computational cost but also performs very well in providing separation rates. This framework could pave the way for the development of simple high-performance diagnostic systems in the future.