Detail publikace

Contribution of different handwriting modalities to differential diagnosis of Parkinson's Disease

Originální název

Contribution of different handwriting modalities to differential diagnosis of Parkinson's Disease

Anglický název

Contribution of different handwriting modalities to differential diagnosis of Parkinson's Disease

Jazyk

en

Originální abstrakt

In this paper, we evaluate the contribution of different handwriting modalities to the diagnosis of Parkinson's disease. We analyse on-surface movement, in-air movement and pressure exerted on the tablet surface. Especially in-air movement and pressure-based features have been rarely taken into account in previous studies. We show that pressure and in-air movement also possess information that is relevant for the diagnosis of Parkinson's Disease (PD) from handwriting. In addition to the conventional kinematic and spatio-temporal features, we present a group of the novel features based on entropy and empirical mode decomposition of the handwriting signal. The presented results indicate that handwriting can be used as biomarker for PD providing classification performance around 89% area under the ROC curve (AUC) for PD classification.

Anglický abstrakt

In this paper, we evaluate the contribution of different handwriting modalities to the diagnosis of Parkinson's disease. We analyse on-surface movement, in-air movement and pressure exerted on the tablet surface. Especially in-air movement and pressure-based features have been rarely taken into account in previous studies. We show that pressure and in-air movement also possess information that is relevant for the diagnosis of Parkinson's Disease (PD) from handwriting. In addition to the conventional kinematic and spatio-temporal features, we present a group of the novel features based on entropy and empirical mode decomposition of the handwriting signal. The presented results indicate that handwriting can be used as biomarker for PD providing classification performance around 89% area under the ROC curve (AUC) for PD classification.

BibTex


@inproceedings{BUT115289,
  author="Peter {Drotár} and Jiří {Mekyska} and Zdeněk {Smékal} and Irena {Rektorová} and Lucia {Masarová} and Marcos {Faúndez Zanuy}",
  title="Contribution of different handwriting modalities to differential diagnosis of Parkinson's Disease",
  annote="In this paper, we evaluate the contribution of different handwriting modalities to the diagnosis of Parkinson's disease. We analyse on-surface movement, in-air movement and pressure exerted on the tablet surface. Especially in-air movement and pressure-based features have been rarely taken into account in previous studies. We show that pressure and in-air movement also possess information that is relevant for the diagnosis of Parkinson's Disease (PD) from handwriting. In addition to the conventional kinematic and spatio-temporal features, we present a group of the novel features based on entropy and empirical mode decomposition of the handwriting signal. The presented results indicate that handwriting can be used as biomarker for PD providing classification performance around 89% area under the ROC curve (AUC) for PD classification.",
  booktitle="Medical Measurements and Applications (MeMeA), 2015 IEEE International Symposium on",
  chapter="115289",
  doi="10.1109/MeMeA.2015.7145225",
  howpublished="print",
  year="2015",
  month="may",
  pages="344--348",
  type="conference paper"
}