Cognitive Performance Deficits and Dysgraphia in Alzheimer’s Disease Patients

Onofri, Emanuela; Mercuri, Marco; Salesi, MariaLucia; Ricciardi, Max Rapp; Archer, Trevor

doi:10.2174/1874220301501010006

Cited by 2 publications

(1 citation statement)

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“…This task is used to test motion control alterations. Tasks #5, #6, #7 and #8 consist in copying words, which is the most explored activity in the analysis of handwriting for people affected by cognitive impairments [14,18,26]. Moreover, to observe the spatial organization ability, the copy of the same word with and without a cue is required.…”

Section: Datasetmentioning

confidence: 99%

Vectorial GP for Alzheimer’s Disease Prediction Through Handwriting Analysis

Azzali

Cilia

Stefano

et al. 2022

Lecture Notes in Computer Science

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Alzheimer's Disease (AD) is a neurodegenerative disease which causes a continuous cognitive decline. This decline has a strong impact on daily life of the people affected and on that of their relatives. Unfortunately, to date there is no cure for this disease. However, its early diagnosis helps to better manage the course of the disease with the treatments currently available. In recent years, AI researchers have become increasingly interested in developing tools for early diagnosis of AD based on handwriting analysis. In most cases, they use a feature engineering approach: domain knowledge by clinicians is used to define the set of features to extract from the raw data. In this paper, we present a novel approach based on vectorial genetic programming (VE GP) to recognize the handwriting of AD patients. VE GP is a recently defined method that enhances Genetic Programming (GP) and is able to directly manage time series in such a way to automatically extract informative features, without any need of human intervention. We applied VE GP to handwriting data in the form of time series consisting of spatial coordinates and pressure. These time series represent pen movements collected from people while performing handwriting tasks. The presented experimental results indicate that the proposed approach is effective for this type of application. Furthermore, VE GP is also able to generate rather small and simple models, that can be read and possibly interpreted. These models are reported and discussed in the last part of the paper.

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Section: Datasetmentioning

confidence: 99%