Name
Abstract | ||
|---|---|---|
Handwriting represents one of the major symptom in Parkinson’s Disease (PD) patients. The computer-aided analysis of the handwriting allows for the identification of promising patterns that might be useful in PD detection and rating. In this study, we propose an innovative set of features extracted by geometrical, dynamical and muscle activation signals acquired during handwriting tasks, and evaluate the contribution of such features in detecting and rating PD by means of artificial neural networks. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1186/s12911-019-0989-3 | BMC Medical Informatics and Decision Making |
Keywords | DocType | Volume |
Handwriting analysis, Model-free, SEMG, Parkinson disease, ANN, MOGA | Journal | 19 |
Issue | ISSN | Citations |
9 | 1472-6947 | 0 |
PageRank | References | Authors |
0.34 | 0 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Giacomo Donato Cascarano | 1 | 5 | 3.16 |
| Claudio Loconsole | 2 | 87 | 14.19 |
| Antonio Brunetti | 3 | 0 | 0.68 |
| Antonio Lattarulo | 4 | 0 | 0.34 |
| Buongiorno, D. | 5 | 46 | 8.35 |
| Giacomo Losavio | 6 | 5 | 1.94 |
| Eugenio Di Sciascio | 7 | 1733 | 147.71 |
| Vitoantonio Bevilacqua | 8 | 468 | 66.40 |