scholarly journals Surface EMG Signal Classification for Parkinson’s Disease using WCC Descriptor and ANN Classifier

2021 ◽  
Author(s):  
Hichem Bengacemi ◽  
Abdenour Hacine-Gharbi ◽  
Philippe Ravier ◽  
Karim Abed-Meraim ◽  
Olivier Buttelli
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Surface Emg ◽  
Signal Classification ◽  
Emg Signal ◽  
Ann Classifier ◽  
Surface Emg Signal

Analysis of surface EMG signal morphology in Parkinson's disease

2007 ◽  
Vol 28 (12) ◽  
pp. 1507-1521 ◽  
Author(s):  
Saara Rissanen ◽  
Markku Kankaanpää ◽  
Mika P Tarvainen ◽  
Juho Nuutinen ◽  
Ina M Tarkka ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Surface Emg ◽  
Emg Signal ◽  
Surface Emg Signal

Surface EMG Signal Classification by Using WPD and Ensemble Tree Classifiers

2017 ◽  
pp. 475-481 ◽  
Author(s):  
Amnah A. Abdullah ◽  
Abdulhamit Subasi ◽  
Saeed Mian Qaisar
Keyword(s):  
Surface Emg ◽  
Signal Classification ◽  
Emg Signal ◽  
Surface Emg Signal

scholarly journals Using Artificial Neural Network to Discriminate Parkinson’s Disease from Other Parkinsonisms by Focusing on Putamen of Dopamine Transporter SPECT Images

Biomedicines ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 12
Author(s):  
Chung-Yao Chien ◽  
Szu-Wei Hsu ◽  
Tsung-Lin Lee ◽  
Pi-Shan Sung ◽  
Chou-Ching Lin
Keyword(s):  
Neural Network ◽  
Parkinson’S Disease ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Parkinson's Disease ◽  
Dopamine Transporter ◽  
Support Vector ◽  
Dat Spect ◽  
Ann Classifier ◽  
Spect Images

Background: The challenge of differentiating, at an early stage, Parkinson’s disease from parkinsonism caused by other disorders remains unsolved. We proposed using an artificial neural network (ANN) to process images of dopamine transporter single-photon emission computed tomography (DAT-SPECT). Methods: Abnormal DAT-SPECT images of subjects with Parkinson’s disease and parkinsonism caused by other disorders were divided into training and test sets. Striatal regions of the images were segmented by using an active contour model and were used as the data to perform transfer learning on a pre-trained ANN to discriminate Parkinson’s disease from parkinsonism caused by other disorders. A support vector machine trained using parameters of semi-quantitative measurements including specific binding ratio and asymmetry index was used for comparison. Results: The predictive accuracy of the ANN classifier (86%) was higher than that of the support vector machine classifier (68%). The sensitivity and specificity of the ANN classifier in predicting Parkinson’s disease were 81.8% and 88.6%, respectively. Conclusions: The ANN classifier outperformed classical biomarkers in differentiating Parkinson’s disease from parkinsonism caused by other disorders. This classifier can be readily included into standalone computer software for clinical application.

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