Effective detection of abnormal gait patterns in Parkinson's disease patients using kinematics, nonlinear, and stability gait features.

Abstract

Parkinson's disease (PD) is a neurodegenerative disease that produces movement disorders and it is the second most common neurodegenerative disease after Alzheimer's. Among other symptoms, PD affects gait patterns and produces bradykinesia, abnormal changes in posture, and shortened strides. In this study we present a comprehensive analysis of three different feature sets to model those abnormal gait patterns. The proposed approach is evaluated upon three groups of subjects: PD patients, young healthy controls (YHC), and elderly healthy controls (EHC). Three feature sets are created: (1) kinematic measures including those that allow modeling time, distance and velocity of the strides, (2) nonlinear dynamics including different measures extracted from embedded attractors resulting from the time-series of the gait signals, and (3) different stability measures extracted in the time and frequency-domains. Support Vector Machine, Random Forest and XGBoost classifiers are trained to automatically discriminate between PD patients and healthy subjects. Among the considered feature sets, three individual measures emerge as the ones that yield accurate detection of PD and could potentially be used in clinical practice. Accuracies of up to 87.0% and 90.0% are found for the classification between PD vs. YHC and PD vs. EHC, respectively, considering individual measures. This study contributes to a better understanding of abnormal gait patterns observed in PD patients. Particularly the introduced approach shows good results that could be potentially used in clinical practice as a tool to support the diagnosis and follow-up of the patients.