Time course of changes in the development of gait disorders in multiple sclerosis

Петров, А. М.; Stolyarov, I. D.; Shkilnyuk, G. G.; Ilves, A. G.; Mineev, K. K.; Lebedev, V. V.; Tsvetkova, T. L.

doi:10.14412/2074-2711-2015-1-27-32

Cited by 3 publications

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Previous studies also suggested a strong relationship between the changes in MPP and body swing, balance, and the sensory functions of the lower extremities 44 . Additionally, increased MPP has also been reported in patients with mild neurological deficits 29 . In the present study, we also found that MPP was more effective in detecting ataxia compared to other parameters ( Fig.…”

Section: Discussionmentioning

confidence: 93%

“…Many studies suggested that MS patients have increased maximum plantar pressure (MPP) [29,30], higher angle [31,32], longer double-support phase [33,34], shorter swing phase, step length, and cadence [13,27,35], as well as alterations in many gait parameters [13,[36][37][38][39] compared to control subjects. In the present study, ataxia was diagnosed based on the evaluation of the parameters shown in ▶ Table 1 by using machine learning methods.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Quantitative Assessment of Ataxia in Multiple Sclerosis Patients using Spatiotemporal Parameters: A Relief-Based Machine Learning Analysis

Bilek

Balgetir

Demir

et al. 2021

Physikalische Medizin, Rehabilitationsmedizin, Kurortmedizin

View full text Add to dashboard Cite

Background and Objective Multiple sclerosis (MS) is a chronic, progressive, and autoimmune disease of the central nervous system (CNS) characterized by inflammation, demyelination, and axonal injury. In patients with newly diagnosed MS (ndMS), ataxia can present either as mild or severe and can be difficult to diagnose in the absence of clinical disability. Such difficulties can be eliminated by using decision support systems supported by machine learning methods. The present study aimed to achieve early diagnosis of ataxia in ndMS patients by using machine learning methods with spatiotemporal parameters. Materials and Methods The prospective study included 32 ndMS patients with an Expanded Disability Status Scale (EDSS) score of≤2.0 and 32 healthy volunteers. A total of 14 parameters were elicited by using a Win-Track platform. The ndMS patients were differentiated from healthy individuals using multiple classifiers including Artificial Neural Network (ANN), Support Vector Machine (SVM), the k-nearest neighbors (K-NN) algorithm, and Decision Tree Learning (DTL). To improve the performance of the classification, a Relief-based feature selection algorithm was applied to select the subset that best represented the whole dataset. Performance evaluation was achieved based on several criteria such as Accuracy (ACC), Sensitivity (SN), Specificity (SP), and Precision (PREC). Results ANN had a higher classification performance compared to other classifiers, whereby it provided an accuracy, sensitivity, and specificity of 89, 87.8, 90.3% with the use of all parameters and provided the values of 93.7, 96.6%, and 91.1% with the use of parameters selected by the Relief algorithm, respectively. Significance To our knowledge, this is the first study of its kind in the literature to investigate the diagnosis of ataxia in ndMS patients by using machine learning methods with spatiotemporal parameters. The proposed method, i. e. Relief-based ANN method, successfully diagnosed ataxia by using a lower number of parameters compared to the numbers of parameters reported in clinical studies, thereby reducing the costs and increasing the performance of the diagnosis. The method also provided higher rates of accuracy, sensitivity, and specificity in the diagnosis of ataxia in ndMS patients compared to other methods. Taken together, these findings indicate that the proposed method could be helpful in the diagnosis of ataxia in minimally impaired ndMS patients and could be a pathfinder for future studies.

show abstract

Section: Discussionmentioning

confidence: 93%

Section: Discussionmentioning

confidence: 99%

Quantitative Assessment of Ataxia in Multiple Sclerosis Patients using Spatiotemporal Parameters: A Relief-Based Machine Learning Analysis

Bilek

Balgetir

Demir

et al. 2021

Physikalische Medizin, Rehabilitationsmedizin, Kurortmedizin

View full text Add to dashboard Cite

show abstract

Efficacy and safety of Kinezia (fampridine) in the complex therapy of multiple sclerosis

Stolyarov

Петров

Boyko

2020

Z. nevrol. psikhiatr. im. S.S. Korsakova

View full text Add to dashboard Cite

Assessment of biomechanical parameters of feet in patients with multiple sclerosis during walking

Dorokhov,

Ivashkova,

Ilves

et al. 2023

РНЖ

View full text Add to dashboard Cite

Rationale. Walking and balance disorders, modi fi ed foot muscles kinematics and walking stereotypes are typical for the patients with Multiple Sclerosis (MS).Objective. To assess the biomechanical parameters of the feet in MS patients with diff erent neurological status during walking.Material and methods. Data analysis of 102 patients with relapsing-remitting MS was carried out for two groups depending on the value of the Expanded Disability Status Scale (EDSS). Plantar pressure distribution was measured on with the pedographic platform emed, novel gmbh, Munich, Germany, using a “fi rst step” protocol.Results and conclusion. Loading of the heel, the area of central metatarsal heads is decreased and loading of the toes is increased during walking in MS patients. Diff erences were found for the spreading coeffi cient (the ratio of the width to the length of the foot), the dynamic width of the foot in patients with an EDSS ≥ 4 is signi fi cantly less in the instep, in the middle and in the narrowest parts of the foot, which leads to the decrease of the contact area. It has been shown that patients with MS who are mobile or walk without assistance are more likely to have pes cavus foot compared to MS patients with minor impairments. The load of the medial side of the foot is greater than the lateral side, which indicates the predominance of the valgus foot. The pronation-supination index is signi fi cantly higher in the patients with moderate impairments in toe-off phase. Evaluation of foot deformity based on the measurement of biomechanical parameters of plantar pressure distribution makes it possible to plan the treatment and rehabilitation tactics in MS patients.

show abstract

Time course of changes in the development of gait disorders in multiple sclerosis

Cited by 3 publications

References 7 publications

Quantitative Assessment of Ataxia in Multiple Sclerosis Patients using Spatiotemporal Parameters: A Relief-Based Machine Learning Analysis

Quantitative Assessment of Ataxia in Multiple Sclerosis Patients using Spatiotemporal Parameters: A Relief-Based Machine Learning Analysis

Efficacy and safety of Kinezia (fampridine) in the complex therapy of multiple sclerosis

Assessment of biomechanical parameters of feet in patients with multiple sclerosis during walking

Contact Info

Product

Resources

About