Evaluation of handwriting kinematics and pressure for differential diagnosis of Parkinson's disease
Language English Country Netherlands Media print-electronic
Document type Journal Article, Research Support, Non-U.S. Gov't
PubMed
26874552
DOI
10.1016/j.artmed.2016.01.004
PII: S0933-3657(16)00006-3
Knihovny.cz E-resources
- Keywords
- Decision support system, Handwriting database, Handwriting pressure, PD dysgraphia, Parkinson's disease, Support vector machine classifier,
- MeSH
- Biomechanical Phenomena * MeSH
- Diagnosis, Differential MeSH
- Middle Aged MeSH
- Humans MeSH
- Parkinson Disease diagnosis MeSH
- Handwriting * MeSH
- Aged MeSH
- Case-Control Studies MeSH
- Support Vector Machine MeSH
- Pressure MeSH
- Check Tag
- Middle Aged MeSH
- Humans MeSH
- Aged MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
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 Pacc=81.3% (sensitivity Psen=87.4% and specificity of Pspe=80.9%). When evaluated separately, pressure features proved to be relevant for PD diagnosis, yielding Pacc=82.5% compared to Pacc=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.
References provided by Crossref.org
A Comparative Study of In-Air Trajectories at Short and Long Distances in Online Handwriting
