Simulation of surgery effect on cerebral palsy gait by supervised machine learning

Abstract : Cerebral Palsy frequently leads to gait troubles. After a physical examination and a Clinical Gait Analysis (CGA), these walking troubles are usually treated by orthopedic surgery, called single event multi-level surgery (SEMLS), in which several surgical corrections are simultaneously done at different levels of the lower limbs. Kinematic improvements obtained by this treatment are sometimes very efficient, but at this moment they remain difficultly predictable. The objective of this thesis is to simulate the effect of surgery on gait parameters, using supervised statisticalmachine learning. The purpose of the simulator is to show the most likely gait outcome in order to improve decision-making in SEMLS. The database was composed of 134 children with cerebral palsy that have undergone surgery and have had at least one CGA before and after the treatment. Gait signals were preprocessed and physical examination missing data were imputed. Features of the preprocessed data were extracted using different techniques such ascurve fitting, variable selection and dimensionality reduction. Then regressions were performed utilizing different methods such as multiple linear regression, feedforward neural networks and ensemble learning. The tested methods and their performances were compared between them andto other methods in the literature. This work represents the first time that the effect of surgery on cerebral palsy gait is quantitatively simulated for a large number of surgical combinations and numerous different gait patterns.
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C. Omar A. Galarraga. Simulation of surgery effect on cerebral palsy gait by supervised machine learning. Machine Learning [cs.LG]. Université Paris-Saclay; Université d'Evry-Val-d'Essonne, 2017. English. ⟨NNT : 2017SACLE006⟩. ⟨tel-01761893⟩

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