Lognormal Features for Early Diagnosis of Alzheimer’s Disease Through Handwriting Analysis

Abstract

Alzheimer’s disease causes most of dementia cases. Although currently there is no cure for this disease, predicting the cognitive decline of people at the first stage of the disease allows clinicians to alleviate its burden. Clinicians evaluate individuals’ cognitive decline by using neuropsychological tests consisting of different sections, each devoted to test a specific set of cognitive skills. The sigma-lognormal model allows complex movements to be represented as a summation of simple time-overlapped movements, and has been used in several fields to model numerous human movements such as, for example, handwriting and speech. Recently, this theory has been also used for detecting and monitoring neurodegenerative disorders. In this paper, we present the results of a preliminary study aimed at exploring the use of lognormal features to classify patients affected by Alzheimer’s disease. The promising results achieved confirms that lognormal features can be used to support Alzheimer’s diagnosis.