Identification and monitoring of Parkinson’s disease dysgraphia based on fractional-order derivatives of online handwriting
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....
| Autores: | , , , , , , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2018 |
| País: | España |
| Institución: | TecnoCampus |
| Repositorio: | Repositori Digital del TecnoCampus |
| OAI Identifier: | oai:repositori.tecnocampus.cat:20.500.12367/2522 |
| Acceso en línea: | http://hdl.handle.net/20.500.12367/2522 |
| Access Level: | acceso abierto |
| Palabra clave: | Parkinson’s disease dysgraphia Micrographia Online handwriting Kinematic analysis Fractional-order derivative Fractional calculus |
| Sumario: | 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. [...] |
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