CDTW-based classification for Parkinson's Disease diagnosis
Résumé
This paper presents a new classification approach for Parkinson’s Disease (PD) diagnosis using Continuous Dynamic Time Warping (CDTW) technique and gait cycles data. These data are the vertical Ground Reaction Forces (vGRFs) recordings collected from eight force sensors placed in each shoe sole worn by each subject. The proposed approach exploits the principle of the repetition of gait cycle patterns to discriminate healthy subjects from PD subjects. The repetition of gait cycles is evaluated using the similarity of the time-series corresponding to stance phases estimated by applying the CDTW technique. The CDTW distances, extracted from gait cycles, are used as inputs of a binary classifier discriminating healthy subjects from PD subjects. Different classification methods are evaluated, including four supervised methods: K-Nearest Neighbours (K-NN), Decision Tree (DT), Random Forest (RF), and Support Vector Machines (SVM), and two unsupervised ones: Gaussian Mixture Model (GMM), and K-means. The proposed approach compares favorably with a classification based on standard features.