Surface EMG and acceleration signals in Parkinson’s disease: feature extraction and cluster analysis

2008 ◽  
Vol 46 (9) ◽  
pp. 849-858 ◽  
Author(s):  
Saara M. Rissanen ◽  
Markku Kankaanpää ◽  
Alexander Meigal ◽  
Mika P. Tarvainen ◽  
Juho Nuutinen ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Cluster Analysis ◽  
Parkinson's Disease ◽  
Feature Extraction ◽  
Surface Emg ◽  
And Cluster Analysis ◽  
Acceleration Signals

scholarly journals What is the true discharge rate and pattern of the striatal projection neurons in Parkinson’s disease and Dystonia?

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Dan Valsky ◽  
Shai Heiman Grosberg ◽  
Zvi Israel ◽  
Thomas Boraud ◽  
Hagai Bergman ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Cluster Analysis ◽  
Parkinson's Disease ◽  
Deep Brain Stimulation ◽  
Discharge Rate ◽  
Spontaneous Discharge ◽  
Projection Neurons ◽  
Pathological State ◽  
Striatal Projection Neurons ◽  
Deep Brain

Dopamine and striatal dysfunctions play a key role in the pathophysiology of Parkinson’s disease (PD) and Dystonia, but our understanding of the changes in the discharge rate and pattern of striatal projection neurons (SPNs) remains limited. Here, we recorded and examined multi-unit signals from the striatum of PD and dystonic patients undergoing deep brain stimulation surgeries. Contrary to earlier human findings, we found no drastic changes in the spontaneous discharge of the well-isolated and stationary SPNs of the PD patients compared to the dystonic patients or to the normal levels of striatal activity reported in healthy animals. Moreover, cluster analysis using SPN discharge properties did not characterize two well-separated SPN subpopulations, indicating no SPN subpopulation-specific (D1 or D2 SPNs) discharge alterations in the pathological state. Our results imply that small to moderate changes in spontaneous SPN discharge related to PD and Dystonia are likely amplified by basal ganglia downstream structures.

Comprehensive analysis of resting tremor based on acceleration signals of patients with Parkinson’s disease

2021 ◽  
pp. 1-13
Author(s):  
Sen Liu ◽  
Han Yuan ◽  
Jiali Liu ◽  
Hai Lin ◽  
Cuiwei Yang ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Rating Scale ◽  
Statistical Tests ◽  
Real Life ◽  
Feature Analysis ◽  
Support Vector ◽  
Monitoring Methods ◽  
Resting Tremor ◽  
Acceleration Signals

BACKGROUND: Resting tremor is an essential characteristic in patients suffering from Parkinson’s disease (PD). OBJECTIVE: Quantification and monitoring of tremor severity is clinically important to help achieve medication or rehabilitation guidance in daily monitoring. METHODS: Wrist-worn tri-axial accelerometers were utilized to record the long-term acceleration signals of PD patients with different tremor severities rated by Unified Parkinson’s Disease Rating Scale (UPDRS). Based on the extracted features, three kinds of classifiers were used to identify different tremor severities. Statistical tests were further designed for the feature analysis. RESULTS: The support vector machine (SVM) achieved the best performance with an overall accuracy of 94.84%. Additional feature analysis indicated the validity of the proposed feature combination and revealed the importance of different features in differentiating tremor severities. CONCLUSION: The present work obtains a high-accuracy classification in tremor severity, which is expected to play a crucial role in PD treatment and symptom monitoring in real life.

scholarly journals Analysis of EMG and Acceleration Signals for Quantifying the Effects of Deep Brain Stimulation in Parkinson’s Disease

2011 ◽  
Vol 58 (9) ◽  
pp. 2545-2553 ◽  
Author(s):  
S. M. Rissanen ◽  
M. Kankaanpaä ◽  
M. P. Tarvainen ◽  
V. Novak ◽  
P. Novak ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Brain Stimulation ◽  
Brain Stimulation ◽  
Acceleration Signals ◽  
Deep Brain
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