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Background: Multiple sclerosis (MS) is a chronic disorder of the central nervous system that affects various parts of the brain and the spinal cord, leading to interruptions of the nervous, defense and movement systems, which usually affect balance and gait. Considering that the diagnosis of MS and its classification is a function of the expertise of the physician, the use of creative methods can help physicians to diagnose and classify different levels of the disease. Methods: The primary objective of the present study was to detect different levels of MS disease based on the nonlinear evaluation of body features. To do so, we studied eight MS patients and posture information of these patients such as the center of pressure (COP) were recorded at different levels with various degrees of Expanded Disability Status Scale (EDSS) by a motion analyzer device, while subjects were standing on the force plate in the eyes-opened and eyes-closed modes. After extracting and validating features that are used to assess posture disorders and explain the balancing behavior, the support vector machine (SVM) was employed to classify different levels of disease. Using the Spearman correlation test, each feature evaluated by the EDSS test. Results: The features obtained from Higuchi’s fractal dimensional algorithm in both anterior-posterior and mediolateral directions of the COP, which were significant (P < 0.05) were selected and provided to SVM and neural network for classification of different levels. It found that SVM outperformed neural network and was able to carry out the classification with the accuracy of 90.7%. Conclusion: As an intelligent method, the non-linear evaluation of body features such as dimensional fractal analysis of the COP can help physicians diagnose different levels of MS with greater precision.

Detection of Different Levels of Multiple Sclerosis by Assessing Nonlinear Characteristics of Posture