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

Kód výsledku v 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>

Nalezeny alternativní kódy

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

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

FH - Neurologie, neurochirurgie, neurovědy

OECD FORD obor

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Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2016

Kód důvěrnosti údajů

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

Název periodika

ARTIFICIAL INTELLIGENCE IN MEDICINE

ISSN

0933-3657

e-ISSN

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Svazek periodika

67

Číslo periodika v rámci svazku

February

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

8

Strana od-do

39-46

Kód UT WoS článku

000374078900003

EID výsledku v databázi Scopus

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