Detail publikace

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

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

Jazyk

en

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.

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",
  annote="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.",
  address="IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA",
  booktitle="2013 IEEE 13TH INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOENGINEERING (BIBE)",
  chapter="108791",
  howpublished="electronic, physical medium",
  institution="IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA",
  year="2013",
  month="november",
  pages="1--4",
  publisher="IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA",
  type="conference paper"
}