Abstract
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 anteriorposterior 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.
Highlights
In the normal state of standing, the mediolateral oscillation controlled, and the human body acts as an inverse pendulum so that in the event of any disturbance, it keeps itself from falling
Chagdes et al in a 2016 paper on limited oscillations in the standing position of human body used Pitchfork and Hopf algorithms with reverse pendulum model, together with a reinforcing gain in the feedback loop of the human control model, presenting a mathematical model that could demonstrate stable and unstable areas of human posture. They revealed that learning about the limited cycle of posture oscillations could aid the diagnosis of musculoskeletal disorders, and considering that the cycle of oscillations associated with specific parameters of the musculoskeletal system, it can be effective for the treatment of this disease.[12]
This study aims to evaluate the nonlinear features of the posture to identify various levels of Multiple sclerosis (MS) disease
Summary
In the normal state of standing, the mediolateral oscillation controlled, and the human body acts as an inverse pendulum so that in the event of any disturbance, it keeps itself from falling. This model was able to distinguish MS patients from other patients.[11] Chagdes et al in a 2016 paper on limited oscillations in the standing position of human body used Pitchfork and Hopf algorithms with reverse pendulum model (representing body muscles), together with a reinforcing gain in the feedback loop of the human control model, presenting a mathematical model that could demonstrate stable and unstable areas of human posture They revealed that learning about the limited cycle of posture oscillations could aid the diagnosis of musculoskeletal disorders, and considering that the cycle of oscillations associated with specific parameters of the musculoskeletal system, it can be effective for the treatment of this disease.[12] One common problem of MS patients is lack of balance in performing voluntary and routine tasks. The characteristics describing person’s ability to assess balance in MS patients extracted and the validity of each characteristic checked by Spearman correlation and EDSS test to provide the classifier with effective features for distinguishing different levels of the disease
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