Evaluation of handwriting kinematics and pressure for differential diagnosis of Parkinson's disease

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00159816%3A_____%2F16%3A00066187" target="_blank" >RIV/00159816:_____/16:00066187 - isvavai.cz</a>

Alternative codes found

RIV/00216224:14110/16:00088939 RIV/00216305:26220/16:PU118769

Result on the web

<a href="http://dx.doi.org/10.1016/j.artmed.2016.01.004" target="_blank" >http://dx.doi.org/10.1016/j.artmed.2016.01.004</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.artmed.2016.01.004" target="_blank" >10.1016/j.artmed.2016.01.004</a>

Result language

angličtina

Original language name

Evaluation of handwriting kinematics and pressure for differential diagnosis of Parkinson's disease

Original language description

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.

Czech name

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Czech description

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Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

FH - Neurology, neuro-surgery, nuero-sciences

OECD FORD branch

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Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2016

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

ARTIFICIAL INTELLIGENCE IN MEDICINE

ISSN

0933-3657

e-ISSN

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Volume of the periodical

67

Issue of the periodical within the volume

February

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

8

Pages from-to

39-46

UT code for WoS article

000374078900003

EID of the result in the Scopus database

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