Analysis of Dynamic Voluntary Muscle Contractions in Parkinson's Disease

Abstract

A novel method for discrimination of dynamic muscle contractions between patients with Parkinson's disease (PD) and healthy controls on the basis of surface electromyography (EMG) and acceleration measurements is presented. In this method, dynamic EMG and acceleration measurements are analyzed using nonlinear methods and wavelets. Ten parameters capturing Parkinson's disease (PD) characteristic features in the measured signals are extracted. Each parameter is computed as time-varying, and for elbow flexion and extension movements separately. For discrimination between subjects, the dimensionality of the feature vectors formed from these parameters is reduced using a principal component approach. The cluster analysis of the low-dimensional feature vectors is then performed for flexion and extension movements separately. The EMG and acceleration data measured from 49 patients with PD and 59 healthy controls are used for analysis. According to clustering results, the method could discriminate 80 % of patient extension movements from 87 % of control extension movements, and 73 % of patient flexion movements from 82 % of control flexion movements. The results show that dynamic EMG and acceleration measurements can be informative for assessing neuromuscular dysfunction in PD, and furthermore, they may help in the objective clinical assessment of the disease.

Feature Extraction Methods for Studying Surface Electromyography and Kinematic Measurements in Parkinson’s Disease

“…objective methods for discriminating between PD patients and healthy subjects on the basis of surface EMG signal morphology [32] and on the basis of simultaneous EMG and acceleration (ACC) recordings during isometric [28] and dynamic muscle contractions [29]. Another aim was to develop methods based on surface EMG and kinematic measurements and analysis for quantifying effects of PD treatment (medication and DBS) on individual level.…”

“…All of those studies presented an innovative approach, that combines a principal component (PC) -based method with a set of effective signal features, for analyzing the EMG and acceleration signals in PD. In the following sections 2, 3 and 4, we describe the methods that were developed and used for feature extraction and discrimination between subjects in [28][29][30][31][32]. All methods were tested with the measured data.…”

“…We analyzed simultaneous EMG and acceleration measurements of PD patients and healthy subjects in [28,29] and aimed to develop methods for discriminating between the patients and the healthy subjects on the basis of the measured signals. The signals were measured during isometric contraction of BB muscles [28] and during dynamic elbow flexion-extension movements [29].…”

“…The signals were measured during isometric contraction of BB muscles [28] and during dynamic elbow flexion-extension movements [29].…”

“…">Our approach for studying surface EMG and kinematic measurements in PDIn order to extract PD-related information from the surface EMG signals effectively, we proposed specific methods based on signal morphology, nonlinear dynamics and wavelets for analyzing the EMG signals of PD patients in [28][29][30][31][32]. One aim of those studies was to develop 222…”