Detail publikace

Decision support framework for Parkinsons disease based on novel handwriting markers

Originální název

Decision support framework for Parkinsons disease based on novel handwriting markers

Anglický název

Decision support framework for Parkinsons disease based on novel handwriting markers

Jazyk

en

Originální abstrakt

Parkinsons disease (PD) is a neurodegenerative disorder which impairs motor skills, speech, and other functions such as behavior, mood, and cognitive processes. One of the most typical clinical hallmarks of PD is handwriting deterioration, usually the first manifestation of PD. The aim of this study is twofold: (a) to find a subset of handwriting features suitable for identifying subjects with PD and (b) to build a predictive model to efficiently diagnose PD. We collected handwriting samples from 37 medicated PD patients and 38 age- and sex- matched controls. The handwriting samples were collected during seven tasks such as writing a syllable, word, or sentence. Every sample was used to extract the handwriting measures. In addition to conventional kinematic and spatio-temporal handwriting measures, we also computed novel handwriting measures based on entropy, signal energy, and empirical mode decomposition of the handwriting signals. The selected features were fed to the support vector machine classifier with radial Gaussian kernel for automated diagnosis. The accuracy of the classification of PD was as high as 88:13%, with the highest values of sensitivity and specificity equal to 89:47% and 91:89%, respectively. Handwriting may be a valuable marker as a diagnostic and screening tool

Anglický abstrakt

Parkinsons disease (PD) is a neurodegenerative disorder which impairs motor skills, speech, and other functions such as behavior, mood, and cognitive processes. One of the most typical clinical hallmarks of PD is handwriting deterioration, usually the first manifestation of PD. The aim of this study is twofold: (a) to find a subset of handwriting features suitable for identifying subjects with PD and (b) to build a predictive model to efficiently diagnose PD. We collected handwriting samples from 37 medicated PD patients and 38 age- and sex- matched controls. The handwriting samples were collected during seven tasks such as writing a syllable, word, or sentence. Every sample was used to extract the handwriting measures. In addition to conventional kinematic and spatio-temporal handwriting measures, we also computed novel handwriting measures based on entropy, signal energy, and empirical mode decomposition of the handwriting signals. The selected features were fed to the support vector machine classifier with radial Gaussian kernel for automated diagnosis. The accuracy of the classification of PD was as high as 88:13%, with the highest values of sensitivity and specificity equal to 89:47% and 91:89%, respectively. Handwriting may be a valuable marker as a diagnostic and screening tool

BibTex


@article{BUT110178,
  author="Peter {Drotár} and Jiří {Mekyska} and Irena {Rektorová} and Lucia {Masarová} and Zdeněk {Smékal} and Marcos {Faúndez Zanuy}",
  title="Decision support framework for Parkinsons disease based on novel handwriting markers",
  annote="Parkinsons disease (PD) is a neurodegenerative
disorder which impairs motor skills, speech, and other functions
such as behavior, mood, and cognitive processes. One of the most
typical clinical hallmarks of PD is handwriting deterioration,
usually the first manifestation of PD. The aim of this study is
twofold: (a) to find a subset of handwriting features suitable for
identifying subjects with PD and (b) to build a predictive model to
efficiently diagnose PD. We collected handwriting samples from
37 medicated PD patients and 38 age- and sex- matched controls.
The handwriting samples were collected during seven tasks such
as writing a syllable, word, or sentence. Every sample was used
to extract the handwriting measures. In addition to conventional
kinematic and spatio-temporal handwriting measures, we also
computed novel handwriting measures based on entropy, signal
energy, and empirical mode decomposition of the handwriting
signals. The selected features were fed to the support vector
machine classifier with radial Gaussian kernel for automated
diagnosis. The accuracy of the classification of PD was as high
as 88:13%, with the highest values of sensitivity and specificity
equal to 89:47% and 91:89%, respectively. Handwriting may be
a valuable marker as a diagnostic and screening tool",
  address="IEEE",
  chapter="110178",
  doi="10.1109/TNSRE.2014.2359997",
  howpublished="print",
  institution="IEEE",
  number="3",
  volume="23",
  year="2015",
  month="may",
  pages="508--516",
  publisher="IEEE",
  type="journal article"
}