Interpretable classification for multivariate gait analysis of cerebral palsy

Abstract

Background: The Gross Motor Function Classification System (GMFCS) is a widely used tool for assessing the mobility of people with Cerebral Palsy (CP). It classifies patients into different levels based on their gross motor function and its level is typically determined through visual evaluation by a trained expert. Although gait analysis is commonly used in CP research, the functional aspects of gait patterns has yet to be fully exploited. By utilizing the gait patterns to predict GMFCS, we can gain a more comprehensive understanding of how CP affects mobility and develop more effective interventions for CP patients. Result: In this study, we propose a multivariate functional classification method to examine the relationship between kinematic gait measures and GMFCS levels in both normal individuals and CP patients with varying GMFCS levels. A sparse linear functional discrimination framework is utilized to achieve an interpretable prediction model. The method is generalized to handle multivariate functional data and multi-class classification. Our method offers competitive or improved prediction accuracy compared to state-of-the-art functional classification approaches and provides interpretable discriminant functions that can characterize the kinesiological progression of gait corresponding to higher GMFCS levels. Conclusion: We generalize the sparse functional linear discrimination framework to achieve interpretable classification of GMFCS levels using kinematic gait measures. The findings of this research will aid clinicians in diagnosing CP and assigning appropriate GMFCS levels in a more consistent, systematic, and scientifically supported manner.