From aging to early-stage Alzheimer's: Uncovering handwriting multimodal behaviors by semi-supervised learning and sequential representation learning

Yacoubi, Mounîm El; Garcia-Salicetti, Sonia; Kahindo, Christian; Rigaud, Anne‐Sophie; Cristancho-Lacroix, Victoria

doi:10.1016/j.patcog.2018.07.029

Cited by 32 publications

(21 citation statements)

References 59 publications

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“…In the future, we will address these limitations by (1) incorporating other online signatures captured during another task in the ALWRITE project; (2) considering other kinematic parameters such as speed, acceleration, and jerk, successfully used for AD assessment based on handwriting after a local point-wise feature extraction [ 15 , 16 ]; and (3) combining SE with other non-linear measures to propose a more refined detection of alterations in signatures due to the disease.…”

Section: Discussionmentioning

confidence: 99%

“…Our private dataset was acquired at Broca Hospital in Paris within the framework of the ALWRITE project [ 15 , 16 ], a French research project on the analysis of different handwriting tasks for AD assessment. All participants freely signed a consent form after receiving information about the study.…”

Section: Materials and Data Acquisition Protocolmentioning

confidence: 99%

“…Few researchers have examined handwriting signatures for assessing the health state of individuals. Prior studies on handwriting focused on other handwriting tasks, such as loops, letters, and handwritten texts, for characterizing and detecting different pathologies, such as Parkinson’s disease [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 ], Huntington’s [ 9 ], Alzheimer’s [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ], and depression [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

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Online Signature Analysis for Characterizing Early Stage Alzheimer’s Disease: A Feasibility Study

Wang

Abazid

Houmani

et al. 2019

Entropy

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We aimed to explore the online signature modality for characterizing early-stage Alzheimer’s disease (AD). A few studies have explored this modality, whereas many on online handwriting have been published. We focused on the analysis of raw temporal functions acquired by the digitizer on signatures produced during a simulated check-filling task. Sample entropy was exploited to measure the information content in raw time sequences. We show that signatures of early-stage AD patients have lower information content than those of healthy persons, especially in the time sequences of pen pressure and pen altitude angle with respect to the tablet. The combination of entropy values on two signatures for each person was classified with two linear classifiers often used in the literature: support vector machine and linear discriminant analysis. The improvements in sensitivity and specificity were significant with respect to the a priori group probabilities in our population of AD patients and healthy subjects. We show that altitude angle, when combined with pen pressure, conveys crucial information on the wrist-hand-finger system during signature production for pathology detection.

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Section: Discussionmentioning

confidence: 99%

Section: Materials and Data Acquisition Protocolmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Online Signature Analysis for Characterizing Early Stage Alzheimer’s Disease: A Feasibility Study

Wang

Abazid

Houmani

et al. 2019

Entropy

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“…El-Yacoubi et al [107] proposed a novel paradigm for studying handwriting changes due to cognitive decline (or aging) by addressing the major limitations of the state-of-the-art solutions. The first one is the assumption of a unique behavioural trend for each cognitive profile.…”

Section: Disease Diagnosismentioning

confidence: 99%

Dynamic Handwriting Analysis for Neurodegenerative Disease Assessment: A Literary Review

Vessio

2019

Applied Sciences

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Studying the effects of neurodegeneration on handwriting has emerged as an interdisciplinary research topic and has attracted considerable interest from psychologists to neuroscientists and from physicians to computer scientists. The complexity of handwriting, in fact, appears to be sensitive to age-related impairments in cognitive functioning; thus, analyzing handwriting in elderly people may facilitate the diagnosis and monitoring of these impairments. A large body of knowledge has been collected in the last thirty years thanks to the advent of new technologies which allow researchers to investigate not only the static characteristics of handwriting but also especially the dynamic aspects of the handwriting process. The present paper aims at providing an overview of the most relevant literature investigating the application of dynamic handwriting analysis in neurodegenerative disease assessment. The focus, in particular, is on Parkinon’s disease (PD) and Alzheimer’s disease (AD), as the two most widespread neurodegenerative disorders. More specifically, the studies taken into account are grouped in accordance with three main research questions: disease insight, disease monitoring, and disease diagnosis. The net result is that dynamic handwriting analysis is a powerful, noninvasive, and low-cost tool for real-time diagnosis and follow-up of PD and AD. In conclusion of the paper, open issues still demanding further research are highlighted.

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“…For what specifically concerns cognitive impairment evaluation, particularly of Alzheimer's type, the effectiveness of handwriting features in supporting the disease detection has been recently shown, e.g. [31]- [33].…”

Section: Related Workmentioning

confidence: 99%

Attentional Pattern Classification for Automatic Dementia Detection

et al. 2019

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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.

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From aging to early-stage Alzheimer's: Uncovering handwriting multimodal behaviors by semi-supervised learning and sequential representation learning

Cited by 32 publications

References 59 publications

Online Signature Analysis for Characterizing Early Stage Alzheimer’s Disease: A Feasibility Study

Online Signature Analysis for Characterizing Early Stage Alzheimer’s Disease: A Feasibility Study

Dynamic Handwriting Analysis for Neurodegenerative Disease Assessment: A Literary Review

Attentional Pattern Classification for Automatic Dementia Detection

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