Detail publikačního výsledku

A New Modality for Quantitative Evaluation of Parkinson's Disease: In-Air Movement

DROTÁR, P.; MEKYSKA, J.; REKTOROVÁ, I.; MASAROVÁ, L.; SMÉKAL, Z.; FAÚNDEZ ZANUY, M.

Originální název

A New Modality for Quantitative Evaluation of Parkinson's Disease: In-Air Movement

Anglický název

A New Modality for Quantitative Evaluation of Parkinson's Disease: In-Air Movement

Druh

Stať ve sborníku v databázi WoS či Scopus

Originální abstrakt

Parkinsons disease (PD) is neurodegenerative disorder with very high prevalence rate occurring mainly among elderly. One of the most typical symptoms of PD is deterioration of handwriting that is usually the first manifestation of Parkinsons disease. In this study, a new modality - in-air trajectory during handwriting - is proposed to efficiently diagnose PD. Experimental results showed that analysis of in-air trajectories is capable of assessing subtle motor abnormalities that are connected with PD. Moreover, conjunction of in-air trajectories with conventional on-surface handwriting allows us to build predictive model with PD classification accuracy over 80%. In total, we compute over 600 handwriting features. Then, we select smaller subset of these features using two feature selection algorithms: Mann-Whitney U-test filter and relief algorithm, and map these feature subsets to binary classification response using support vector machines.

Anglický abstrakt

Parkinsons disease (PD) is neurodegenerative disorder with very high prevalence rate occurring mainly among elderly. One of the most typical symptoms of PD is deterioration of handwriting that is usually the first manifestation of Parkinsons disease. In this study, a new modality - in-air trajectory during handwriting - is proposed to efficiently diagnose PD. Experimental results showed that analysis of in-air trajectories is capable of assessing subtle motor abnormalities that are connected with PD. Moreover, conjunction of in-air trajectories with conventional on-surface handwriting allows us to build predictive model with PD classification accuracy over 80%. In total, we compute over 600 handwriting features. Then, we select smaller subset of these features using two feature selection algorithms: Mann-Whitney U-test filter and relief algorithm, and map these feature subsets to binary classification response using support vector machines.

Klíčová slova

handwriting

Klíčová slova v angličtině

handwriting

Autoři

DROTÁR, P.; MEKYSKA, J.; REKTOROVÁ, I.; MASAROVÁ, L.; SMÉKAL, Z.; FAÚNDEZ ZANUY, M.

Rok RIV

2017

Vydáno

10.11.2013

Nakladatel

IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA

Místo

NEW YORK, NY 10017 USA

ISBN

978-1-4799-3163-7

Kniha

2013 IEEE 13TH INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOENGINEERING (BIBE)

Strany od

1

Strany do

4

Strany počet

4

BibTex

@inproceedings{BUT108791,
  author="Peter {Drotár} and Jiří {Mekyska} and Irena {Rektorová} and Lucia {Masarová} and Zdeněk {Smékal} and Marcos {Faúndez Zanuy}",
  title="A New Modality for Quantitative Evaluation of Parkinson's Disease: In-Air Movement",
  booktitle="2013 IEEE 13TH INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOENGINEERING (BIBE)",
  year="2013",
  pages="1--4",
  publisher="IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA",
  address="NEW YORK, NY 10017 USA",
  isbn="978-1-4799-3163-7"
}