Probabilistic gait classification in children with cerebral palsy: A Bayesian approach

Three-dimensional gait analysis (3DGA) generates a wealth of highly variable data. Gait classifications help to reduce, simplify and interpret this vast amount of 3DGA data and thereby assist and facilitate clinical decision making in the treatment of CP. CP gait is often a mix of several clinically accepted distinct gait patterns. Therefore, there is a need for a classification which characterizes each CP gait by different degrees of membership for several gait patterns, which are considered by clinical experts to be highly relevant. In this respect, this paper introduces Bayesian networks (BN) as a new approach for classification of 3DGA data of the ankle and knee in children with CP. A BN is a probabilistic graphical model that represents a set of random variables and their conditional dependencies via a directed acyclic graph. Furthermore, they provide an explicit way of introducing clinical expertise as prior knowledge to guide the BN in its analysis of the data and the underlying clinically relevant relationships. BNs also enable to classify gait on a continuum of patterns, as their outcome consists of a set of probabilistic membership values for different clinically accepted patterns. A group of 139 patients with CP was recruited and divided into a training- (n = 80% of all patients) and a validation-dataset (n = 20% of all patients). An average classification accuracy of 88.4% was reached. The BN of this study achieved promising accuracy rates and was found to be successful for classifying ankle and knee joint motion on a continuum of different clinically relevant gait patterns.

► Preprocessing of 3D gait analysis data is required to facilitate clinical decision-making in the treatment of cerebral palsy (CP).
► Bayesian networks are a promising tool for clinically relevant preprocessing of 3D gait data in children with CP.
► This study applied Bayesian networks to probabilistically classify gait in CP children on a continuum of clinically relevant patterns.
► An average classification accuracy of 88.4% was reached.
► Bayesian networks were thus found to be a successful tool for preprocessing 3D gait data.