Significance of Principal Component Analysis (PCA) in characterization of gait pattern in parkinsonian patients
Introduction: PCA of parameters of gait pattern is a mathematic tool to reduce the redundant information from these parameters. In addition it is a powerful instrument to identify pathological gait pattern. Aim of our study was to analyse the significance of PCA in parkinsonian gait.
Material and Methods: We analysed gait pattern during walking on a tread mill in 21 parkinsonian patients and 16 healthy controls using a system of Zebris Medical GmbH. The measurement included 3 different gait velocities (individual normal – slow – fast). Range of movements of the following joints were analysed: shoulder/upper arm – elbow – hip/thigh – knee. With these parameters PCA was done.
Results: Gait can be sufficiently characterized in healthy subjects as well as in parkinsonian patients using 4 main components (P1-P4) from PCA. P1 represents the essential part of all components in both groups. How ever in the patients group amount of P1 is clearly reduced in comparison to healthy controls. The other components (P2-P4) are clearly increased in patients. The amount of P1 depends on gait velocity in healthy subjects. This was not found in the more severely affected parkinsonian patients (UPDRS >20).
Conclusion: PCA allows the reduction of data in complex gait patterns and is a useful instrument to describe pathological gait pattern.