Fractional Derivatives of Online Handwriting: A New Approach of Parkinsonic Dysgraphia Analysis

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

doi:10.1109/tsp.2018.8441293

Cited by 17 publications

(18 citation statements)

References 23 publications

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“…To the best of our knowledge, except for our pilot work [20], there are no prior studies which integrate FD into a handwriting parameterization for quantitative PD dysgraphia analysis. Therefore, the results published in this paper are exploratory in nature.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, FC-based analytical tools have outperformed classical techniques in geology [36,37], economics and finance [38,39], etc. Moreover, in our recent paper [20], we identified a high potential for the use of FC in the kinematic analysis of PD drawings. Based on these preliminary results, we assume that FD-based handwriting features may bring improvements to PD diagnosis and assessment.…”

Section: Introductionmentioning

confidence: 98%

“…Several research teams have explored the impact of quantitative PD dysgraphia analysis utilizing simple handwriting/drawing tasks (e.g., separate characters, a combination of two or three characters, repetitive loops, circles), as well as more complex ones (e.g., words, sentences, figures, 3D objects, and the Archimedean spiral) [8,[16][17][18][19][20]. An overview of recent related works (2015-present) can be seen in Table 1.…”

Section: Introductionmentioning

confidence: 99%

“…In 2018, Impedovo et al reported the results of a study focused only on the early stages of PD; the best accuracy was 74.76% for a combination of three handwriting tasks. Finally, in our previous work [20], we proposed a new approach of advanced kinematic feature extraction that utilizes fractional-order derivatives (FD). This approach increased the classification accuracy by 10% (72.39%) for Archimedean spiral tasks in comparison with the baseline [20].…”

Section: Introductionmentioning

confidence: 99%

“…Finally, in our previous work [20], we proposed a new approach of advanced kinematic feature extraction that utilizes fractional-order derivatives (FD). This approach increased the classification accuracy by 10% (72.39%) for Archimedean spiral tasks in comparison with the baseline [20]. SVM-support vector machine; EMD-empirical mode decomposition; K-NN-K-nearest neighbors; ANOVA-analysis of variance; NB-naïve Bayes classifier; OPF-optimum path forest; ANN-artificial neural network; CNN-convolutional neural network; RF-random forests; LDA-Linear Discriminant Analysis; ADA-AdaBoost; AUC-area under the receiver operating characteristics (ROC) curve; articles are sorted by the year of release and then alphabetically; * analyzes performed on the same database (Parkinson's disease handwriting database (PaHaW) [8]).…”

Section: Introductionmentioning

confidence: 99%

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Identification and Monitoring of Parkinson’s Disease Dysgraphia Based on Fractional-Order Derivatives of Online Handwriting

et al. 2018

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Parkinson’s disease dysgraphia affects the majority of Parkinson’s disease (PD) patients and is the result of handwriting abnormalities mainly caused by motor dysfunctions. Several effective approaches to quantitative PD dysgraphia analysis, such as online handwriting processing, have been utilized. In this study, we aim to deeply explore the impact of advanced online handwriting parameterization based on fractional-order derivatives (FD) on the PD dysgraphia diagnosis and its monitoring. For this purpose, we used 33 PD patients and 36 healthy controls from the PaHaW (PD handwriting database). Partial correlation analysis (Spearman’s and Pearson’s) was performed to investigate the relationship between the newly designed features and patients’ clinical data. Next, the discrimination power of the FD features was evaluated by a binary classification analysis. Finally, regression models were trained to explore the new features’ ability to assess the progress and severity of PD. These results were compared to a baseline, which is based on conventional online handwriting features. In comparison with the conventional parameters, the FD handwriting features correlated more significantly with the patients’ clinical characteristics and provided a more accurate assessment of PD severity (error around 12%). On the other hand, the highest classification accuracy (ACC = 97.14%) was obtained by the conventional parameters. The results of this study suggest that utilization of FD in combination with properly selected tasks (continuous and/or repetitive, such as the Archimedean spiral) could improve computerized PD severity assessment.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Identification and Monitoring of Parkinson’s Disease Dysgraphia Based on Fractional-Order Derivatives of Online Handwriting

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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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On the Use of First and Second Derivative Approximations for Biometric Online Signature Recognition

Faundez-Zanuy,

Diaz

2023

Lecture Notes in Computer Science

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This paper investigates the impact of different approximation methods in feature extraction for pattern recognition applications, specifically focused on delta and delta-delta parameters. Using MCYT330 online signature database, our experiments show that 11-point approximation outperforms 1-point approximation, resulting in a 1.4% improvement in identification rate, 36.8% reduction in random forgeries and 2.4% reduction in skilled forgeries

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Fractional Derivatives of Online Handwriting: A New Approach of Parkinsonic Dysgraphia Analysis

Cited by 17 publications

References 23 publications

Identification and Monitoring of Parkinson’s Disease Dysgraphia Based on Fractional-Order Derivatives of Online Handwriting

Identification and Monitoring of Parkinson’s Disease Dysgraphia Based on Fractional-Order Derivatives of Online Handwriting

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

On the Use of First and Second Derivative Approximations for Biometric Online Signature Recognition

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