Please use this identifier to cite or link to this item: https://scholarhub.balamand.edu.lb/handle/uob/562
Title: Feature selection for an improved Parkinson's disease identification based on handwriting
Authors: Taleb, Catherine
Khachab, Maha 
Mokbel, Chafic 
Affiliations: Faculty of Medicine 
Department of Electrical Engineering 
Keywords: Feature extraction
Feature selection
Image classification
Kinematics
Medical disorders
Medical image processing
Operating system kernels
Statistical analysis
Support Vector Machine (SVM)
Subjects: Cognition
Disease
Neurophysiologie
Issue Date: 2017
Publisher: IEEE
Part of: First IEEE International Workshop on Arabic Script Analysis and Recognition
Start page: 1
End page: 5
Conference: International Workshop on Arabic Script Analysis and Recognition (ASAR) (1st : 3-5 April 2017 : Nancy, France) 
Abstract: 
Parkinson's disease (PD) is a neurological disorder associated with a progressive decline in motor skills, speech, and cognitive processes. Since the diagnosis of Parkinson's disease is difficult, researchers have worked to develop a support tool based on algorithms to separate healthy controls from PD patients. Online handwriting analysis is one of the methods that can be used to diagnose PD. The purpose of this study is to find a subset of handwriting features suitable for efficiently identifying subjects with PD. Data was taken from PDMultiMC database collected in Lebanon, and consisting of 16 medicated PD patients and 16 age matched controls. Seven handwriting tasks were collected such as copying patterns, copying words in Arabic, and writing full names. For each task kinematic and spatio-temporal, pressure, energy, entropy, and intrinsic features were extracted. Feature selection was done in two stages; the first stage selected a subset using statistical analysis, and the second step selected the most relevant features of this subset by a suboptimal approach. The selected features were fed to a support vector machine classifier with RBF kernel, whose aim is to identify the subjects suffering from PD. The accuracy of the classification of PD was as high as 96.875%, with sensitivity and specificity equal to 93.75 % and 100% respectively. The results as well as the selected features suggest that handwriting can be a valuable marker as a PD diagnosis tool.
URI: https://scholarhub.balamand.edu.lb/handle/uob/562
Open URL: Link to full text
Type: Conference Paper
Appears in Collections:Faculty of Medicine

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