Attentional Pattern Classification for Automatic Dementia Detection

Abstract

This paper proposes a novel technique for the 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 an input to machine learning algorithms to automatize 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 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 area under the ROC curve (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 mass screening of the population.INDEX TERMS Machine learning, decision support systems, medical diagnosis.

Dynamic Handwriting Analysis for Neurodegenerative Disease Assessment: A Literary Review

“…The Attentional Matrice test, instead, is a cancellation test in which the subject is asked to mark target digits assigned among several distractors. In Reference [42], it was shown how the perceptual decision while scanning, easily captured by in-air movement analysis, is impaired in cognitively deteriorated subjects.…”

“…In Reference [42], Angelillo et al proposed a digitized version of the Attentional Matrices Test for selective attention assessment: it is based on three matrices of increasing difficulty, and the subject is asked to the mark target digits assigned. The authors observed how, although a pathological matrix may look "normal" if considering only the on-surface pattern, the information provided by in-air movements reveal a completely deteriorated search strategy of the targets to be marked among the distractors.…”

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“…Novel insights could be obtained, for example, by using a multi-expert approach or a set of fuzzy inference rules. Promising results from using these techniques for diagnostic purposes in other domains have been recently reported, e.g., [52,53]. A multi-expert approach, which is based on ensembling different classifiers trained for the same task, may further improve prediction accuracy, as it can benefit from the different viewpoints from which these classifiers may look at the data.…”

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“…Novel features like sensor (digital) data, handwriting (text), and speech (audio), offer unique opportunities to identify new indicators of cognitive decline (Kourtis et al, 2019). The five exemplar studies in Table 1 include home-based motion sensors (Akl et al, 2015), computerized handwriting analyzes (Angelillo et al, 2019), videotaped handwashing tasks (Ashraf and Taati, 2016), multi-modal wearable activity monitors (Gwak et al, 2018), and audio-recorded speech data (Toth et al, 2018) for detecting cognitive impairment. These data (particularly environmental and wearable sensors) have the potential for continuous, longitudinal tracking of cognitive changes.…”