This paper proposes a novel technique for an automatic detection of dementia based on the Attentional Matrices test (AMT) for selective attention assessment. The original test provides three matrices, of increasing difficulty, and the test taker is asked to mark target digits assigned. In our proposal, AMT was developed on a digitizing tablet, equipped with an electronic pen. Tablet technology enables the acquisition of additional measures to those that can be obtained by observing the execution of the traditional paper-based test. These measures reflect the dynamics of the handwriting process, particularly the pauses and hesitations while the pen is not in contact with the pad surface. Handwriting measures can then serve as input to machine learning algorithms to automatize the disease detection. In contrast to the traditional approach, dynamic handwriting analysis can provide a means to better evaluate the visual search of the patient, as well as her motor planning. To evaluate the effectiveness of the proposal, a classification study was carried out involving 29 healthy control subjects and 36 demented patients. We employed different machine learning algorithms and an ensemble scheme. We observed the first matrix to be the most discriminating; while, the ensemble of the best classification models over the three matrices provided the best classification performance (i.e., an AUC of 87.30% and a sensitivity of 86.11%). Our proposal has the potential to provide a cost-effective and easy-to-use diagnostic tool, which may also support a mass screening of the population.

Attentional Pattern Classification for Automatic Dementia Detection

MARIA TERESA ANGELILLO;FABRIZIO BALDUCCI;DONATO IMPEDOVO;GIUSEPPE PIRLO;GENNARO VESSIO
2019

Abstract

This paper proposes a novel technique for an automatic detection of dementia based on the Attentional Matrices test (AMT) for selective attention assessment. The original test provides three matrices, of increasing difficulty, and the test taker is asked to mark target digits assigned. In our proposal, AMT was developed on a digitizing tablet, equipped with an electronic pen. Tablet technology enables the acquisition of additional measures to those that can be obtained by observing the execution of the traditional paper-based test. These measures reflect the dynamics of the handwriting process, particularly the pauses and hesitations while the pen is not in contact with the pad surface. Handwriting measures can then serve as input to machine learning algorithms to automatize the disease detection. In contrast to the traditional approach, dynamic handwriting analysis can provide a means to better evaluate the visual search of the patient, as well as her motor planning. To evaluate the effectiveness of the proposal, a classification study was carried out involving 29 healthy control subjects and 36 demented patients. We employed different machine learning algorithms and an ensemble scheme. We observed the first matrix to be the most discriminating; while, the ensemble of the best classification models over the three matrices provided the best classification performance (i.e., an AUC of 87.30% and a sensitivity of 86.11%). Our proposal has the potential to provide a cost-effective and easy-to-use diagnostic tool, which may also support a mass screening of the population.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11586/229842
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