Fractional Derivatives of Online Handwriting: a New Approach of Parkinsonic Dysgraphia Analysis
| Authors | |
|---|---|
| Year of publication | 2018 |
| Type | Article in Proceedings |
| Conference | 2018 41ST INTERNATIONAL CONFERENCE ON TELECOMMUNICATIONS AND SIGNAL PROCESSING (TSP) |
| MU Faculty or unit | |
| Citation | |
| Doi | http://dx.doi.org/10.1109/TSP.2018.8441293 |
| Keywords | Archimedean spiral; binary classification; fractal calculus; fractional derivative; online handwriting; Parkinson's disease |
| Description | Parkinson's disease (PD) is the second most frequent neurodegenerative disorder. One typical hallmark of PD is disruption in execution of practised skills such as handwriting. This paper introduces a new methodology of kinematic features calculation based on fractional derivatives applied on PD handwriting. Discrimination power of basic kinematic features (velocity, acceleration, jerk) was evaluated by classification analysis (using support vector machines and random forests). For this purpose, 30 PD patients and 36 healthy controls were enrolled. In comparison with results reported in other works, the newly designed features based on fractional derivatives increased classification accuracy by 8% in univariate analysis and by 10% when employing the multivariate one. This study reveals an impact of fractional derivatives based features in analysis of Parkinsonic dysgraphia. |
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