Because of the rich set of spatiotemporal features it allows to extract, online handwriting is being increasingly investigated for characterizing neurodegenerative diseases like Parkinson and Alzheimer. The state of the art on the latter is dominated by methods that extract global (average) kinematic parameters, and then apply basic classification techniques or standard statistical tests to assess the statistical significance of each parameter in discriminating a pathological population from a healthy control one.

We propose a new approach for characterizing Early-Stage Alzheimer disease (ES-AD), and Mild Cognitive Impairment (MCI) w.r.t Healthy Controls (HC) that, instead of considering average kinematic HW parameters, which discards the dynamics related to each subject, is based on a semi-global parameterization scheme encoding the distribution of each kinematic parameter over a fixed number of bins. Such a distribution characterizes the gross dynamics associated with each parameter. A semi-supervised learning is proposed, in which a Normalized Mutual Information (NMI) selection scheme guides a hierarchical clustering algorithm to choose the best tradeoff between the number of clusters and the discriminative power of each w.r.t to the three cognitive profiles.

For both global and semi-global parameters, the semi-supervised learning scheme uncovers clusters with two trends, one cluster that consists essentially of HC and MCI, and one cluster essentially composed of MCI and ES-AD. The clusters obtained with semi-global parameters are more informative than those with global parameters as reflected by a better NMI value.

A semi-global parametrization of handwriting spatiotemporal parameters allows for a better discrimination between the HC, MCI and ES-AD profiles, than a global one does. Unlike the latter, the former encodes the distribution of the dynamics of each parameter, which offers a larger parameter space in which discrimination is easier.