Graphical representation and variability quantification of handwriting signals: New tools for Parkinson’s disease detection

Abstract

A reliable computer-aided method for Parkinson’s disease (PD) detection can slow down its progression and improve the life quality of patients. In this study, a new non-invasive and cost-effective method based on the online analysis of handwriting signals has been proposed. First, the dynamic handwriting signals have been converted into two graphical representations of the variability rate. Then, two new feasible features, including area of the analytic signal representation and area of the second-order difference plot, have been used to quantify the variability rate of handwriting signals. A statistical test and support vector machine classifier have been applied in a comparative study to test the impact of each variability feature, writing task, and time sequence on the detection performance, separately.The obtained results on PaHaW database with 35 Parkinson’s disease patients and 36 healthy controls have shown that the proposed method of handwriting variability feature extraction has effective performance and the capability for the PD detection. It has achieved an average sensitivity of 86.26% with only two types of features, providing a trade-off between the performance, the computational complexity, and interpretability of the motor patterns from the point of view of clinicians and neuropsychologists. X-coordinate time-series and writing a sentence can achieve superior accuracy and robustness in the presence of individual differences. The experimental results have demonstrated that extracting the variability features that used graphical representations of the global changes in oscillatory mode has the ability to clinically describe the pathological dynamics of the handwriting signals for the PD identification.