A Supervised Classification of Children with Fragile X Syndrome and Controls Based on Kinematic and sEMG Parameters

Abstract

Fragile X syndrome (FXS) is caused by pathologic expansions of the CGG repeat polymorphic region of the FMR1 gene. There are two main categories of FMR1 mutations, “premutation” and “full mutation”, that are associated with different clinical phenotypes, and somatic mosaicism can represent a strong FXS phenotype modulator. FXS is the leading cause of inherited intellectual disability and autism, and it is characterized by musculoskeletal manifestations such as flexible flat feet, joint laxity and hypotonia. The former have been associated with altered joint kinematics and muscle activity during gait. The aim of this study was to use gait analysis parameters to classify FXS children from healthy controls and, within FXS children with full mutation, to classify children with mosaicism. Seven supervised machine learning algorithms were applied to a dataset of joint kinematics and surface electromyographic signals collected on twenty FXS children and sixteen controls. Results showed that the k-NN algorithm outperformed in terms of accuracy (100%) in classifying FXS children from controls, while CN2 rule induction obtained the best accuracy (97%) in classifying FXS children with mosaicism. The proposed pipeline might be used for developing assisted decision-making systems aiming at identifying and treating the musculoskeletal alterations associated with FXS.

Keywords

• supervised learning
• fragile X syndrome
• gait analysis
• surface electromyography
• kinematics