Gait Influence Diagrams in Parkinson's Disease.
Previous studies have shown that gait patterns differ between Parkinson's disease (PD) patients and controls. However, almost all these studies focused only on univariate time series of a single variable. This approach cannot reveal detailed information of foot loading dynamics and the cooperative relationships of different anatomical plantar foot areas when the subjects walk. By contrast, we propose a novel multivariate method for analyzing gait patterns of the PD patients: Gait Influence Diagrams (GIDs). These are constructed by analyzing the Wiener-Akaike-Granger- Schweder influences between vertical ground reaction force signals at different plantar areas of both feet. In this paper, we use the particular case of WAGS influence measures known as "extended Granger causality analysis". GIDs are directed graphs, with arrows indicating those influences that are significantly different between PD patients and healthy subjects. We confirm prior clinical observations that Parkinsonian gait differs significantly from the healthy one in the anterior-posterior movement direction. A new finding is that there are also pathological changes in the lateral-medial direction. Importantly, gait asymmetry for the PD patients is clearly evident in GIDs, even in earlier stages of the disease. These results suggest that GID might be of use in future PD gait pattern studies.
Peng
P
Ren
Esin
E
Karahan
Chao
C
Chen
Ruixue
R
Luo
Yayuan
Y
Geng
Jorge Francisco
JF
Bosch Bayard
Maria L
ML
Bringas
Dezhong
D
Yao
Keith M
KM
Kendrick
Pedro A
PA
Valdes-Sosa
D000368:Aged; D000465:Algorithms; D003196:Computer Graphics; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D020233:Gait Disorders, Neurologic / Q000150:complications / Q000175:diagnosis* / Q000503:physiopathology*; D006801:Humans; D008297:Male; D008875:Middle Aged; D008959:Models, Neurological; D015233:Models, Statistical; D015999:Multivariate Analysis; D010300:Parkinson Disease / Q000150:complications / Q000175:diagnosis* / Q000503:physiopathology*; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity