Analysis of Parkinson’s Disease Dysgraphia Based on Optimized Fractional Order Derivative Features

Mucha, Ján; López-de-Ipiña, Karmele; Faundez-Zanuy, Marcos; Mekyska, Jiří; Zvončák, Vojtěch; Galáž, Zoltán; Kiska, Tomáš; Smékal, Zdeněk; Brabenec, Luboš; Rektorová, Irena

doi:10.23919/eusipco.2019.8903088

Cited by 7 publications

(14 citation statements)

References 19 publications

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“…This shows that FDs can be advantageously applied to both kinematic as well as dynamic features. Additionally, the values of α suggest that regular derivation is not optimal for kinematic handwriting features, which is in line with our previous research [44], [46].…”

Section: Discussionsupporting

confidence: 90%

“…Content may change prior to final publication. focusing on the quantitative analysis of parkinsonian dysgraphia [44]- [47], where they brought a promising improvement in the power of the FD-based features to objectively discriminate between healthy and dysgraphic handwriting using machine learning. In this work, we aim at exploring the possibilities of utilizing FD to describe GD in schoolaged children.…”

Section: ) Fractional Order Derivative Featuresmentioning

confidence: 99%

“…where D α y(t) denotes a derivative with order α of a function y(t), and h represents a sampling lattice. Following our previous works focused on optimization of α [44], [47], we used the ranges: from 0.1 to 0.4, and from 0.65 to 0.9, with iteration step of 0.05.…”

Section: ) Fractional Order Derivative Featuresmentioning

confidence: 99%

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Advanced Parametrization of Graphomotor Difficulties in School-Aged Children

et al. 2020

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School-aged children spend 31-60 % of their time at school performing handwriting, which is a complex perceptual-motor skill composed of a coordinated combination of fine graphomotor movements. As up to 30 % of them experience graphomotor difficulties (GD), timely diagnosis of these difficulties and therapeutic intervention are of great importance. At present, an objective, computerized decision support system for the identification and assessment of GD in school-aged children is still missing. In this study, we propose three novel advanced handwriting parametrization techniques based on modulation spectra, fractional order derivatives, and tunable Q-factor wavelet transform to improve the identification of GD using online handwriting. For this purpose, we analyzed signals acquired from 7 basic graphomotor tasks performed by 53 children attending 3rd and 4th grade at several primary schools around the Czech Republic. Combining the newly proposed features with the conventionally used ones, we were able to identify GD with 84 % accuracy. In this study, we showed that using advanced parametrization of basic graphomotor movements can be potentially used to improve our capabilities of quantifying problems with the development of legible, fast-paced handwriting, and help with the early diagnosis of handwriting difficulties frequently manifested in developmental dysgraphia.

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Section: Discussionsupporting

confidence: 90%

Section: ) Fractional Order Derivative Featuresmentioning

confidence: 99%

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Advanced Parametrization of Graphomotor Difficulties in School-Aged Children

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“…. For more information, see our previous works [12], [14], [28]. An example of the selected combined loop task performed by a child with/without GD can be seen in Fig.…”

Section: Dataset and Methodology A Datasetmentioning

confidence: 99%

“…Considering the success of utilizing the FD (Grünwald-Letnikov approach) in Parkinson's disease dysgraphia analysis in our previous works [9]- [12], and in the assessment of GD in school-aged children [13], [28], this study, as a next logical step, has the following aims:…”

Section: Introductionmentioning

confidence: 99%

Analysis of Various Fractional Order Derivatives Approaches in Assessment of Graphomotor Difficulties

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Exploration of Various Fractional Order Derivatives in Parkinson’s Disease Dysgraphia Analysis

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Galáž

Mekyska

et al. 2022

Lecture Notes in Computer Science

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Parkinson's disease (PD) is a common neurodegenerative disorder with a prevalence rate estimated to 2.0% for people aged over 65 years. Cardinal motor symptoms of PD such as rigidity and bradykinesia affect the muscles involved in the handwriting process resulting in handwriting abnormalities called PD dysgraphia. Nowadays, online handwritten signal (signal with temporal information) acquired by the digitizing tablets is the most advanced approach of graphomotor difficulties analysis. Although the basic kinematic features were proved to effectively quantify the symptoms of PD dysgraphia, a recent research identified that the theory of fractional calculus can be used to improve the graphomotor difficulties analysis. Therefore, in this study, we follow up on our previous research, and we aim to explore the utilization of various approaches of fractional order derivative (FD) in the analysis of PD dysgraphia. For this purpose, we used the repetitive loops task from the Parkinson's disease handwriting database (PaHaW). Handwritten signals were parametrized by the kinematic features employing three FD approximations: Gru ¨nwald-Letnikov's, Riemann-Liouville's, and Caputo's. Results of the correlation analysis revealed a significant relationship between the clinical state and the handwriting features based on the velocity. The extracted features by Caputo's FD approximation outperformed the rest of the analyzed FD approaches. This was also confirmed by the results of the classification analysis, where the best model This work was supported by grant no. NU20-04-00294 (Diagnostics of Lewy body diseases in prodromal stage based on multimodal data analysis) of the Czech Ministry of Health and by Spanish grant of the Ministerio de Ciencia e Innovacio ´n no. PID2020-113242RB-I00 and by EU grant Next Generation EU (project no. LX22NPO5107 (MEYS)).trained by Caputo's handwriting features resulted in a balanced accuracy of 79.73% with a sensitivity of 83.78% and a specificity of 75.68%.

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Analysis of Parkinson’s Disease Dysgraphia Based on Optimized Fractional Order Derivative Features

Cited by 7 publications

References 19 publications

Advanced Parametrization of Graphomotor Difficulties in School-Aged Children

Advanced Parametrization of Graphomotor Difficulties in School-Aged Children

Analysis of Various Fractional Order Derivatives Approaches in Assessment of Graphomotor Difficulties

Exploration of Various Fractional Order Derivatives in Parkinson’s Disease Dysgraphia Analysis

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