Tensiomyography and Statistical Analysis Based Muscle Change Detection in Multiple Sclerosis for Smart Healthcare

Abstract

The impact of demyelinization on muscle fiber changes and the type of changes in multiple sclerosis (MS) is very hard to estimate. One of the major problems of MS patients is muscle fatigue and decrease of muscle force in the range of 16–57%. The objective of this research work is to estimate various aspects of muscle changes at tibial muscle (mTA) level using a noninvasive method named as tensiomyography (TMG). TMG provides information about muscle functions in MS. This study includes 40 MS patients among which 18 are males (45%) and 22 are females (55%). They are divided in two subgroups: subgroup A and subgroup B. Subgroup A includes 20 MS patients without clinical decelable gait disorders and subgroup B includes 20 MS patients with clinical decelable gait disorders. Also, we have a control group that includes 20 healthy people with the same average age. Average age is 38.15 ± 11.19 y for MS patients and 39.34 ± 10.57 for healthy people. Evaluation measures include ADL score and EDSS scale. The ADL score is 0 for patients from subgroup A and 1 for patients from subgroup B. The EDSS score is 1 for subgroup A and 2.5 for subgroup B. This study confirms the importance of TMG based evaluation of muscle changes in MS patients. This smart healthcare system is also used for prediction of the muscle changes and muscle imbalance. Contraction time (Tc) recordings are used to detect the muscle fatigue which is a specific symptom of MS. The value of Tc for subgroup A is 45.8 ms and subgroup B is 61.37 ms for right side. Analysis of these two parameters such as Dm and Tc could define the muscle behaviour and help provide early information about the possibility of developing gait disorders. This smart TMG system analyses the muscle tone in the best possible way to predict the onset of any diseases which is an integral part of the smart healthcare system.