Detail publikace

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

Originální název

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

Anglický název

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

Jazyk

en

Originální abstrakt

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.

Anglický abstrakt

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.

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"
}