Types of gait deviations in children and adolescents with Guillain-Barre syndrome identified using cluster analysis. (April 2021)

• Record Type: Journal Article
• Authors: Syczewska, Małgorzata; Święcicka, Anna; Szczerbik, Ewa; Kalinowska, Małgorzata; Dunin-Wąsowicz, Dorota; Łukowicz, Małgorzata
• Is Part Of: Biomedical signal processing and control. Volume 66(2021)
• Publication Date: 2021-04
• Subjects: Guillain-Barre syndrome -- Gait pattern -- Cluster analysis; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.102496
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Gait pattern is not homogenous in children with Guillain-Barre Syndrome. Cluster analysis enables their division into 3 groups based on their gait pattern. Discriminant analysis confirms high percentage of correctly classified cases: 94 %. Abstract: Objective: Guillain-Barre syndrome (GBS) - inflammatory disorder of peripheral nerves, rare disease occurring suddenly in healthy people with following symptoms: weakness, numbness in limbs, problems while walking. Aim: to describe the 3D gait pattern of children and adolescents with GBS, and to see if their gait pattern is homogenous, or not. Methods: 25 patients, 11 girls and 16 boys, aged from 3 to 17 years old were included. The gait analysis was performed at the time of the diagnosis, and when the treatment and rehabilitation program were started. Gait analysis was performed either using a VICON 460 system with six cameras, or VICON MX system with 12 cameras. In both cases, the Helen Hayes marker set and lower body Plug-In-Gait model were used. Gait data extracted from reports together with Gait Deviation Index, Gillette Gait Index, Gait Variation Scores and Gait Profile Score were taken into analysis. Each of the variables is characterized by high variability: there is no one gait pattern, which can describe these patients. Conclusion: The cluster analysis enabled the division of patients into three groups, based on the closeness of the analysed gait variables and indices. Discriminant analysis confirmed this division, it gave very high percent of correctly classified cases: total 94 % legs were correctly classified. Significance: Despite the similarity of the neurological signs in paediatric patients with GBS and the same treatment regimen, their gait pattern during the recovery time is different.