Fuzzy clustering of children with cerebral palsy based on temporal-distance gait parameters

Abstract
Temporal-distance parameters for 88 children with the spastic diplegia form of cerebral palsy (CP) are grouped using the fuzzy clustering paradigm. The 2 features chosen for clustering are stride length and cadence which are normalized for age and leg length using a model based on a population of 68 neurologically intact children. Using information provided by the neurologically intact population and cluster validity techniques, 5 clusters for the children with cerebral palsy are identified. The 5 cluster centers represent distinct walking strategies adopted by children with cerebral palsy. Utilizing just four easily obtained parameters-stride length, cadence, leg length and age-and a small number of simple equations, it is possible to classify any child with spastic diplegia and to generate an individual's membership values for each of the 5 clusters. The clinical utility of the fuzzy clustering approach is demonstrated with pre- and post-operative test data for subjects with cerebral palsy (one neurosurgical and one orthopaedic) where changes in membership of the 5 clusters provide an objective technique for measuring improvement. This approach can be adopted to study other clinical entities where different cluster centers would be established using the algorithm provided here.

This publication has 22 references indexed in Scilit: