Fuzzy clustering of children with cerebral palsy based on temporal-distance gait parameters.
Temporal-distance parameters for 88 children with the spastic diplegia form of cerebral palsy (CP) are grouped using the fuzzy clustering paradigm. The two 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, five clusters for the children with cerebral palsy are identified. The five 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 five 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 five 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.
O'Malley
M J
MJ
Department of Electronic and Electrical Engineering, University College, Dublin, Ireland.
Abel
M F
MF
Damiano
D L
DL
Vaughan
C L
CL
D000293:Adolescent; D000328:Adult; D000465:Algorithms; D002547:Cerebral Palsy / Q000503:physiopathology*; D002648:Child; D002675:Child, Preschool; D016000:Cluster Analysis; D005260:Female; D005684:Gait; D006801:Humans; D008297:Male; D015233:Models, Statistical