SVM-Based Gait Analysis and Classification for Patients with Parkinson’s Disease

Abstract

The medical diagnosis of Parkinson’s disease (PD) mainly relies on doctors’ clinical experience and scoring scales, which may lead to erroneous judgments due to the differences of their clinical experience. Gait information, such as postural instability and bradykinesia, is a common symptom in PD. Gait analysis and classification is often adopted as a clinical tool for detecting and diagnosing PD. In this paper, gait parameters are collected from 200 PD patients and 100 healthy controls (HC) through the wearable sensors, which include 95 features. Considering the redundancy between the features, F -test and Recursive Feature Elimination (RFE) methods are utilized for feature selection. Moreover, support vector machine (SVM) algorithm is utilized to train the classifier, which can automatically classify PD patients and HC. Furthermore, experiments on the class-imbalance and class-equilibrium samples are performed to verify the reliability and stability of the above methods. Compared with all feature based SVM and F -test based SVM, the RFE-based SVM has better performance, where the accuracy, sensitivity and specificity are 96.67%, 96.77% and 96.55%, respectively. The results show that the RFE-based SVM classifier can well distinguish PD and healthy gait, and can effectively assist doctors to diagnose PD in clinical practice, thus breaking the subjectivity brought by traditional PD diagnosis.