A comparison between ANN and SVM classifiers for Parkinson's disease by using a model-free computer-assisted handwriting analysis based on biometric signals

Patients suffering from Parkinson's Disease (PD) are characterized by an abnormal handwriting activity since they have difficulties in motor coordination and a decline in cognition. In this paper, we propose a model-free technique for differentiating PD patients from healthy subjects by using a handwriting analysis tool based on biometric signals (e.g., surface ElectroMyoGraphy, pen pressure, etc.) and an Artificial Intelligence-based classifier. Experimental tests have been carried on with both healthy and PD subjects to identify the most representative features, the best writing patterns and the best AI-based classification approach between Artificial Neural Network (ANN) and Support Vector Machine (SVM) in terms of accuracy and repeatability. Finally, the obtained results are reported and discussed to infer some important properties on writing patterns, classification approaches and the role of muscular activities on the handwriting analysis applied to neurodegenerative disease research.