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We present an advanced method for feature extraction and cluster analysis of surface electromyograms (EMG) and acceleration signals in Parkinson's disease (PD). In the method, 12 different EMG and acceleration signal features are extracted and used to form high-dimensional feature vectors. The dimensionality of these vectors is then reduced by using the principal component approach. Finally, the cluster analysis of feature vectors is performed in a low-dimensional eigenspace. The method was tested with EMG and acceleration data of 42 patients with PD and 59 healthy controls. The obtained discrimination between patients and controls was promising. According to clustering results, one cluster contained 90% of the healthy controls and two other clusters 76% of the patients. Seven patients with severe motor dysfunctions were distinguished in one of the patient clusters. In the future, the clinical value of the method should be further evaluated.

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.

The purpose of the study was to evaluate linear and nonlinear tremor characteristics of the hand in patients with Parkinson's disease (PD) and to compare the results with those of healthy old and young control subjects. Furthermore, the aim was to study correlation between tremor characteristics and clinical signs. A variety of nonlinear (sample entropy, cross-sample entropy, recurrence rate, determinism and correlation dimension) and linear (amplitude, spectral peak frequency and total power, and coherence) hand tremor parameters were computed from acceleration measurements for PD patients (n = 30, 68.3 ± 7.8 years), and old (n = 20, 64.2 ± 7.0 years) and young (n = 20, 18.4 ± 1.1 years) control subjects. Nonlinear tremor parameters such as determinism, sample entropy and cross-sample entropy were significantly different between the PD patients and healthy controls. These parameters correlated with the Unified Parkinson's disease rating scale (UPDRS), tremor and finger tapping scores, but not with the rigidity scores. Linear tremor parameters such as the amplitude and the maximum power (power corresponding to peak frequency) also correlated with the clinical findings. No major difference was detected in the tremor characteristics between old and young control subjects. The study revealed that tremor in PD patients is more deterministic and regular when compared to old or young healthy controls. The nonlinear tremor parameters can differentiate patients with PD from healthy control subjects and these parameters may have potential in the assessment of the severity of PD (UPDRS).

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