Article

Early-Onset Ataxia (EOA) and Developmental Coordination Disorder (DCD) are two
conditions that affect coordination in children. Phenotypic identification of
impaired coordination plays an important role in their diagnosis. Gait is one of
the tests included in rating scales that can be used to assess motor
coordination. A practical problem is that the resemblance between EOA and DCD
symptoms can hamper their diagnosis. In this study we employed inertial sensors
and a supervised classifier to obtain an automatic classification of the
condition of participants. Data from shank and waist mounted inertial measurement
units were used to extract features during gait in children diagnosed with EOA or
DCD and age-matched controls. We defined a set of features from the recorded
signals and we obtained the optimal features for classification using a backward
sequential approach. We correctly classified 80.0%, 85.7%, and 70.0% of the
control, DCD and EOA children, respectively. Overall, the automatic classifier
correctly classified 78.4% of the participants, which is slightly better than the
phenotypic assessment of gait by two pediatric neurologists (73.0%). These
results demonstrate that automatic classification employing signals from inertial
sensors obtained during gait maybe used as a support tool in the differential
diagnosis of EOA and DCD. Furthermore, future extension of the classifier's test
domains may help to further improve the diagnostic accuracy of pediatric
coordination impairment. In this sense, this study may provide a first step
towards incorporating a clinically objective and viable biomarker for
identification of EOA and DCD.
CI - Copyright (c) 2016 Elsevier B.V. All rights reserved.

Langue : ANGLAIS