Publication detail

Advanced Analysis of Online Handwriting in a Multilingual Cohort of Patients with Parkinson's Disease

MUCHA, J. MEKYSKA, J. FAÚNDEZ ZANUY, M. SANZ-CARTAGENA, M. GALÁŽ, Z. ZVONČÁK, V. KISKA, T. SMÉKAL, Z. LOPEZ-DE-IPINA, K. REKTOROVÁ, I.

Original Title

Advanced Analysis of Online Handwriting in a Multilingual Cohort of Patients with Parkinson's Disease

English Title

Advanced Analysis of Online Handwriting in a Multilingual Cohort of Patients with Parkinson's Disease

Type

conference paper

Language

en

Original Abstract

The majority of Parkinson’s disease (PD) patients suffer from handwriting abnormalities commonly called as Parkinsonic dysgraphia. Several approaches of PD dysgraphia analysis exist, e.g. based on online handwriting processing. However, a small and unilingual cohort of PD patients is often an issue in quantitative PD dysgraphia analysis studies. Therefore, in this work, we aim to perform a discrimination analysis in a multilingual cohort of 73 PD patients and 48 healthy controls (Spanish and Czech). For this purpose, we extracted advanced handwriting features based on fractional order derivatives (FD). Discrimination power of the advanced FD-based features was evaluated by Mann-Whitney U test and random forests classifier. We reached 82 % classification accuracy (86 % sensitivity, 77 % specificity) in the multilingual cohort. In addition, we observed high discrimination power of the FD-based parameters and proofed the high impact of online handwriting processing in cross-cultural PD dysgraphia analysis studies.

English abstract

The majority of Parkinson’s disease (PD) patients suffer from handwriting abnormalities commonly called as Parkinsonic dysgraphia. Several approaches of PD dysgraphia analysis exist, e.g. based on online handwriting processing. However, a small and unilingual cohort of PD patients is often an issue in quantitative PD dysgraphia analysis studies. Therefore, in this work, we aim to perform a discrimination analysis in a multilingual cohort of 73 PD patients and 48 healthy controls (Spanish and Czech). For this purpose, we extracted advanced handwriting features based on fractional order derivatives (FD). Discrimination power of the advanced FD-based features was evaluated by Mann-Whitney U test and random forests classifier. We reached 82 % classification accuracy (86 % sensitivity, 77 % specificity) in the multilingual cohort. In addition, we observed high discrimination power of the FD-based parameters and proofed the high impact of online handwriting processing in cross-cultural PD dysgraphia analysis studies.

Keywords

Parkinsonic dysgraphia, Micrographia, Online handwriting, Fractional order derivative, Fractional calculus, Multilingual cohort.

Released

20.03.2019

Publisher

International Frequency Sensor Association (IFSA) Publishing, S. L.

Location

Barcelona, Spain

ISBN

978-84-09-10127-6

Book

Advances in Signal Processing and Artificial Intelligence: Proceedings of the 1st International Conference on Advances in Signal Processing and Artificial Intelligence

Edition

1

Edition number

1

Pages from

144

Pages to

147

Pages count

4

BibTex


@inproceedings{BUT156340,
  author="Ján {Mucha} and Jiří {Mekyska} and Zoltán {Galáž} and Vojtěch {Zvončák} and Tomáš {Kiska} and Zdeněk {Smékal}",
  title="Advanced Analysis of Online Handwriting in a Multilingual Cohort of Patients with Parkinson's Disease",
  annote="The majority of Parkinson’s disease (PD) patients suffer from handwriting abnormalities commonly called as
Parkinsonic dysgraphia. Several approaches of PD dysgraphia analysis exist, e.g. based on online handwriting processing. However, a small and unilingual cohort of PD patients is often an issue in quantitative PD dysgraphia analysis studies. Therefore, in this work, we aim to perform a discrimination analysis in a multilingual cohort of 73 PD patients and 48 healthy controls (Spanish and Czech). For this purpose, we  extracted advanced handwriting features based on fractional order derivatives (FD). Discrimination power of the advanced FD-based features was evaluated by Mann-Whitney U test and random forests classifier. We reached 82 % classification accuracy (86 % sensitivity, 77 % specificity) in the multilingual cohort. In addition, we observed high discrimination power of the FD-based parameters and proofed the high impact of online handwriting processing in cross-cultural PD dysgraphia analysis studies.",
  address="International Frequency Sensor Association (IFSA) Publishing, S. L.",
  booktitle="Advances in Signal Processing and Artificial Intelligence: Proceedings of the 1st International Conference
on Advances in Signal Processing and Artificial Intelligence",
  chapter="156340",
  edition="1",
  howpublished="online",
  institution="International Frequency Sensor Association (IFSA) Publishing, S. L.",
  year="2019",
  month="march",
  pages="144--147",
  publisher="International Frequency Sensor Association (IFSA) Publishing, S. L.",
  type="conference paper"
}