Classification of cerebral palsy gait by Kernel Fisher Discriminant Analysis

Authors:
Bai-ling Zhang;Yanchun Zhang
Affiliations:
(Correspd. E-mail: bailing.zhang@xjtlu.edu.cn) Department of Computer Science and Software Engineering, Xi'an Jiaotong-Liverpool University, 215123, P.R. China;School of Computer Science and Mathematics, Victoria University, VIC 3011, Australia
Venue:
International Journal of Hybrid Intelligent Systems - Computational Models for Life Sciences
Year:
2008

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Abstract

Cerebral palsy (CP) is generally considered as a non-progressive neuro-developmental condition that occurs in early childhood and is associated with a motor impairment, usually affecting mobility and posture. Automatic accurate identification of cerebral palsy gait has many potential applications, for example, assistance in diagnosis, clinical decision-making and communication among the clinical professionals. In previous studies, support vector machine (SVM) and some other pattern classification methods like neural networks have been applied to classify CP gait patterns. The objective of this study is to first further investigate different classification paradigms in the CP gait analysis, particularly the Kernel Fisher Discriminant Analysis (KFD) which has been successfully applied to many pattern recognition problems and identified as a strong competitor of SVM. The component obtained by KFD maximally separates two classes in the feature space, thus overcoming the limitations of linear discriminant analysis of being unable to extract nonlinear features representing higher-order statistics. Using a publicly available CP gait dataset (68 normal healthy and 88 with spastic diplegia form of CP), a comprehensive performances comparison was presented with different features including the two basic temporal-spatial gait parameters (stride length and cadence). Various cross-validation testing show that the KFD offers better classification accuracies than the support vector machine and is superior to a number of other classification methods such as decision tree, multiple layer perceptron and k nearest neighbor.