Explainable Metrics for the Assessment of Neurodegenerative Diseases through Handwriting Analysis

Abstract

Motor changes are early signs of neurodegenerative diseases (NDs) such as Parkinson's disease (PD) and Alzheimer's disease (AD), but are often difficult to detect, especially in the early stages. In this work, we examine the behavior of a wide array of explainable metrics extracted from the handwriting signals of 113 subjects performing multiple tasks on a digital tablet. The aim is to measure their effectiveness in characterizing and assessing multiple NDs, including AD and PD. To this end, task-agnostic and task-specific metrics are extracted from 14 distinct tasks. Subsequently, through statistical analysis and a series of classification experiments, we investigate which metrics provide greater discriminative power between NDs and healthy controls and among different NDs. Preliminary results indicate that the various tasks at hand can all be effectively leveraged to distinguish between the considered set of NDs, specifically by measuring the stability, the speed of writing, the time spent not writing, and the pressure variations between groups from our handcrafted explainable metrics, which shows p-values lower than 0.0001 for multiple tasks. Using various classification algorithms on the computed metrics, we obtain up to 87% accuracy to discriminate AD and healthy controls (CTL), and up to 69% for PD vs CTL.