Classification of Parkinson's Disease and Essential Tremor Based on Structural MRI

2016 ◽  
Author(s):  
Li Zhang ◽  
Chang Liu ◽  
Xiujun Zhang ◽  
Yuan Yan Tang
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Essential Tremor ◽  
Structural Mri

Machine Learning Classification of Parkinson's Disease and Essential Tremor Using Wearable Sensors

2020 ◽  
Vol 101 (11) ◽  
pp. e44
Author(s):  
Sanghee Moon ◽  
Hyun-Je Song ◽  
Kelly Lyons ◽  
Rajesh Pahwa ◽  
Vibhash Sharma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Essential Tremor ◽  
Wearable Sensors ◽  
Machine Learning Classification

Automated classification of Parkinson's disease and Essential Tremor by combining electromyography and accelerometer signals

Basal Ganglia ◽  
2013 ◽  
Vol 3 (1) ◽  
pp. 61 ◽  
Author(s):  
P. Kugler ◽  
J.C.M. Schlachetzki ◽  
A. Schramm ◽  
J. Winkler ◽  
J. Klucken ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Essential Tremor ◽  
Automated Classification

scholarly journals Classification of Essential Tremor and Parkinson’s Tremor Based on a Low-Power Wearable Device

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1695
Author(s):  
Patrick Locatelli ◽  
Dario Alimonti ◽  
Gianluca Traversi ◽  
Valerio Re
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Essential Tremor ◽  
Wearable Device ◽  
Spectral Features ◽  
Classification Models ◽  
Average Accuracy ◽  
The Right ◽  
Parkinson's Tremor

Among movement disorders, essential tremor is by far the most common, as much as eight times more prevalent than Parkinson’s disease. Although these two conditions differ in their presentation and course, clinicians do not always recognize them, leading to common misdiagnoses. Proper and early diagnosis is important for receiving the right treatment and support. In this paper, the development of a portable and reliable tremor classification system based on a wearable device, enabling clinicians to differentiate between essential tremor and Parkinson’s disease-associated one, is reported. Inertial data were collected from subjects with a well-established diagnosis of tremor, and analyzed to extract different sets of relevant spectral features. Supervised learning methods were then applied to build several classification models, among which the best ones achieved an average accuracy above 90%. Results encourage the use of wearable technology as effective and affordable tools to support clinicians.

Multiclass machine learning classification of functional brain images for Parkinson's disease stage prediction

2020 ◽  
Vol 13 (5) ◽  
pp. 508-523 ◽  
Author(s):  
Guan‐Hua Huang ◽  
Chih‐Hsuan Lin ◽  
Yu‐Ren Cai ◽  
Tai‐Been Chen ◽  
Shih‐Yen Hsu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Disease Stage ◽  
Brain Images ◽  
Functional Brain ◽  
Machine Learning Classification
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