Parkinson's disease feature extraction

2019 ◽  
pp. 103-115
Author(s):  
Pooi Chi Quah ◽  
Wai Yie Leong
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Feature Extraction

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 Hybrid Feature Extraction for Detection of Degree of Motor Fluctuation Severity in Parkinson’s Disease Patients

Entropy ◽  
2019 ◽  
Vol 21 (2) ◽  
pp. 137 ◽  
Author(s):  
Murtadha D. Hssayeni ◽  
Joohi Jimenez-Shahed ◽  
Behnaz Ghoraani
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Feature Extraction ◽  
Motor Function ◽  
Motor Fluctuation ◽  
Classification Model ◽  
Sensor Data ◽  
Body Movements ◽  
Hybrid Feature Extraction ◽  
Free Body

The success of medication adjustment in Parkinson’s disease (PD) patients with motor fluctuation relies on the knowledge about their fluctuation severity. However, because of the temporal and spatial variability in motor fluctuations, a single clinical examination often fails to capture the spectrum of motor impairment experienced in routine daily life. In this study, we developed an algorithm to estimate the degree of motor fluctuation severity from two wearable sensors’ data during subjects’ free body movements. Specifically, we developed a new hybrid feature extraction method to represent the longitudinal changes of motor function from the sensor data. Next, we developed a classification model based on random forest to learn the changes in the patterns of the sensor data as the severity of the motor function changes. We evaluated our algorithm using data from 24 subjects with idiopathic PD as they performed a variety of daily routine activities. A leave-one-subject-out assessment of the algorithm resulted in 83.33% accuracy, indicating that our approach holds a great promise to passively detect degree of motor fluctuation severity from continuous monitoring of an individual’s free body movements. Such a sensor-based assessment system and algorithm combination could provide the objective and comprehensive information about the fluctuation severity that can be used by the treating physician to effectively adjust therapy for PD patients with troublesome motor fluctuation.

scholarly journals Quantitative susceptibility mapping based hybrid feature extraction for diagnosis of Parkinson's disease

2019 ◽  
Vol 24 ◽  
pp. 102070 ◽  
Author(s):  
Bin Xiao ◽  
Naying He ◽  
Qian Wang ◽  
Zenghui Cheng ◽  
Yining Jiao ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Feature Extraction ◽  
Susceptibility Mapping ◽  
Quantitative Susceptibility Mapping ◽  
Hybrid Feature Extraction

scholarly journals 3D Textural, Morphological and Statistical Analysis of Voxel of Interests in 3T MRI Scans for the Detection of Parkinson’s Disease Using Artificial Neural Networks

Healthcare ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 34
Author(s):  
Sabyasachi Chakraborty ◽  
Satyabrata Aich ◽  
Hee-Cheol Kim
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Feature Extraction ◽  
Dopaminergic Neurons ◽  
Extraction Process ◽  
Machine Learning Algorithms ◽  
Subcortical Structures ◽  
Progressive Loss ◽  
Mri Scans

Parkinson’s disease is caused due to the progressive loss of dopaminergic neurons in the substantia nigra pars compacta (SNc). Presently, with the exponential growth of the aging population across the world the number of people being affected by the disease is also increasing and it imposes a huge economic burden on the governments. However, to date, no therapy or treatment has been found that can completely eradicate the disease. Therefore, early detection of Parkinson’s disease is very important so that the progressive loss of dopaminergic neurons can be controlled to provide the patients with a better life. In this study, 3T T1-MRI scans were collected from 906 subjects, out of which, 203 are control subjects, 66 are prodromal subjects and 637 are Parkinson’s disease patients. To analyze the MRI scans for the detection of neurodegeneration and Parkinson’s disease, eight subcortical structures were segmented from the acquired MRI scans using atlas based segmentation. Further, on the extracted eight subcortical structures, feature extraction was performed to extract textural, morphological and statistical features, respectively. After the feature extraction process, an exhaustive set of 107 features were generated for each MRI scan. Therefore, a two-level feature extraction process was implemented for finding the best possible feature set for the detection of Parkinson’s disease. The two-level feature extraction procedure leveraged correlation analysis and recursive feature elimination, which at the end provided us with 20 best performing features out of the extracted 107 features. Further, all the features were trained using machine learning algorithms and a comparative analysis was performed between four different machine learning algorithms based on the selected performance metrics. And at the end, it was observed that artificial neural network (multi-layer perceptron) performed the best by providing an overall accuracy of 95.3%, overall recall of 95.41%, overall precision of 97.28% and f1-score of 94%, respectively.

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