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Conference papers

Feature selection for an improved Parkinson's disease identification based on handwriting

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 differentiate healthy controls from PD patients. Online handwriting analysis is one of the methods that can be used to diagnose PD. The aim 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 select 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%. The results as well as the selected features suggest that handwriting can be a valuable marker as a diagnosis tool.
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Contributor : Laurence Likforman-Sulem Connect in order to contact the contributor
Submitted on : Thursday, December 5, 2019 - 4:00:36 PM
Last modification on : Saturday, June 25, 2022 - 9:13:33 PM
Long-term archiving on: : Friday, March 6, 2020 - 5:31:41 PM


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Catherine Taleb, Laurence Likforman-Sulem, Maha Khachab, Chafic Mokbel. Feature selection for an improved Parkinson's disease identification based on handwriting. 2017 1st International Workshop on Arabic Script Analysis and Recognition (ASAR 2017), Apr 2017, Nancy, France. pp.52-56, ⟨10.1109/ASAR.2017.8067759⟩. ⟨hal-02395677⟩



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