期刊
ARTIFICIAL INTELLIGENCE IN MEDICINE
卷 67, 期 -, 页码 39-46出版社
ELSEVIER
DOI: 10.1016/j.artmed.2016.01.004
关键词
Decision support system; Support vector machine classifier; Handwriting database; Handwriting pressure; Parkinson's disease; PD dysgraphia
资金
- European Regional Development Fund [COST IC 1206, NT13499, LO1401, CZ.1.07/2.3.00/20.0094, CZ.1.05/1.1.00/02.0068]
- FEDER
- Ministerio de Economa y Competitividad [TEC2012-38630-C04-03]
- Brno University of Technology [CZ.1.07/2.3.00/30.0005]
Objective: We present the PaHaW Parkinson's disease handwriting database, consisting of handwriting samples from Parkinson's disease (PD) patients and healthy controls. Our goal is to show that kinematic features and pressure features in handwriting can be used for the differential diagnosis of PD. Methods and material: The database contains records from 37 PD patients and 38 healthy controls performing eight different handwriting tasks. The tasks include drawing an Archimedean spiral, repetitively writing orthographically simple syllables and words, and writing of a sentence. In addition to the conventional kinematic features related to the dynamics of handwriting, we investigated new pressure features based on the pressure exerted on the writing surface. To discriminate between PD patients and healthy subjects, three different classifiers were compared: K-nearest neighbors (K-NN), ensemble AdaBoost classifier, and support vector machines (SVM). Results: For predicting PD based on kinematic and pressure features of handwriting, the best performing model was SVM with classification accuracy of P-acc = 81.3% (sensitivity P-sen = 87.4% and specificity of P-spe = 80.9%). When evaluated separately, pressure features proved to be relevant for PD diagnosis, yielding P-acc = 82.5% compared to P-acc = 75.4% using kinematic features. Conclusion: Experimental results showed that an analysis of kinematic and pressure features during handwriting can help assess subtle characteristics of handwriting and discriminate between PD patients and healthy controls. (C) 2016 Elsevier B.V. All rights reserved.
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