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

Mucha, Ján; Mekyska, Jiří; Galáž, Zoltán; Faundez-Zanuy, Marcos; Zvončák, Vojtěch; Šafárová, Katarína; Urbánek, Tomáš; Havigerová, Jana Marie; Bednářová, Jiřina; Smékal, Zdeněk

doi:10.1109/access.2020.3042591

Cited by 5 publications

(4 citation statements)

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“…Regarding the results of the correlation analysis, the most significantly correlated handwriting features (after the FDR adjustment) were extracted by the Caputo's FD. This observation is in line with our previous results [24], where we analysed the same three FD approaches in assessment of the graphomotor difficulties in school-aged children. The performance of the handwriting features extracted by the Riemann-Liouville's approach is almost as good as the Caputo's features.…”

Section: Discussionsupporting

confidence: 92%

“…In addition, the FC has been widely utilized in several computer vision disciplines such as the super-resolution, motion estimation, image restoration or image segmentation [34]. Furthermore, in our recent research we developed new handwriting features extraction techniques based on the application of the fractional order derivatives (FD) [11,[21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

“…Newly designed handwriting features achieved classification accuracy up to 90%, using the Gru ¨nwald-Letnikov approach only. In addition to PD dysgraphia analysis, we explored the FD-based handwriting features in analysis of graphomotor difficulties in school-aged children, where we examined three different FD approaches [24]. The results suggests that the employment of various FD approximations brings major differences in kinematic handwriting features.…”

Section: Introductionmentioning

confidence: 99%

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

Mucha

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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Section: Discussionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Mucha

Galáž

Mekyska

et al. 2022

Lecture Notes in Computer Science

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“…Despite the broad use and indisputable success of these conventional handcrafted features, our recent studies (Mucha et al, 2018a , b ; Mucha et al, 2019 ) concerning the computerized identification and assessment of PD and developmental dysgraphia (DD) have illustrated the necessity of additional research into novel and more advanced parametrization techniques for handwriting that could enable more robust and complex characterization of HD. For this reason, various nonlinear handwriting features based on modulation spectra, fractional-order derivatives (FD) and the tunable-Q wavelet transform have been developed and evaluated (Galaz et al, 2020 ; Mucha et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Comparison of CNN-Learned vs. Handcrafted Features for Detection of Parkinson's Disease Dysgraphia in a Multilingual Dataset

Galáž

Drotár

Mekyska

et al. 2022

Front. Neuroinform.

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Parkinson's disease dysgraphia (PDYS), one of the earliest signs of Parkinson's disease (PD), has been researched as a promising biomarker of PD and as the target of a noninvasive and inexpensive approach to monitoring the progress of the disease. However, although several approaches to supportive PDYS diagnosis have been proposed (mainly based on handcrafted features (HF) extracted from online handwriting or the utilization of deep neural networks), it remains unclear which approach provides the highest discrimination power and how these approaches can be transferred between different datasets and languages. This study aims to compare classification performance based on two types of features: features automatically extracted by a pretrained convolutional neural network (CNN) and HF designed by human experts. Both approaches are evaluated on a multilingual dataset collected from 143 PD patients and 151 healthy controls in the Czech Republic, United States, Colombia, and Hungary. The subjects performed the spiral drawing task (SDT; a language-independent task) and the sentence writing task (SWT; a language-dependent task). Models based on logistic regression and gradient boosting were trained in several scenarios, specifically single language (SL), leave one language out (LOLO), and all languages combined (ALC). We found that the HF slightly outperformed the CNN-extracted features in all considered evaluation scenarios for the SWT. In detail, the following balanced accuracy (BACC) scores were achieved: SL—0.65 (HF), 0.58 (CNN); LOLO—0.65 (HF), 0.57 (CNN); and ALC—0.69 (HF), 0.66 (CNN). However, in the case of the SDT, features extracted by a CNN provided competitive results: SL—0.66 (HF), 0.62 (CNN); LOLO—0.56 (HF), 0.54 (CNN); and ALC—0.60 (HF), 0.60 (CNN). In summary, regarding the SWT, the HF outperformed the CNN-extracted features over 6% (mean BACC of 0.66 for HF, and 0.60 for CNN). In the case of the SDT, both feature sets provided almost identical classification performance (mean BACC of 0.60 for HF, and 0.58 for CNN).

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A new accurate method for solving fractional relaxation-oscillation with Hilfer derivatives

et al. 2022

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

Cited by 5 publications

References 32 publications

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

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

Comparison of CNN-Learned vs. Handcrafted Features for Detection of Parkinson's Disease Dysgraphia in a Multilingual Dataset

A new accurate method for solving fractional relaxation-oscillation with Hilfer derivatives

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