Characterizing Early-Stage Alzheimer Through Spatiotemporal Dynamics of Handwriting

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

doi:10.1109/lsp.2018.2794500

Cited by 37 publications

(41 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

See 2 more Smart Citations

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

Wang

Abazid

Houmani

et al. 2019

Entropy

Self Cite

View full text Add to dashboard Cite

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.

show abstract

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

Self Cite

View full text Add to dashboard Cite

show abstract

“…An approach similar to the previous one was followed by the authors in Reference [108]. In particular, they modeled the velocity trajectory of loop-like movements through a temporal clustering based on dynamic time warping as a dissimilarity measure.…”

Section: Disease Diagnosismentioning

confidence: 99%

Dynamic Handwriting Analysis for Neurodegenerative Disease Assessment: A Literary Review

Vessio

2019

Applied Sciences

View full text Add to dashboard Cite

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.

show abstract

“…It is well known that handwriting problems are related to this disease as well as to its severity, so changes in writing can be considered a prominent biomarker [1], [2]. Handwriting, in fact, is a complex activity entailing cognitive, kinesthetic and perceptualmotor components [3], whose changes can be used for the evaluation of PD [4]- [6] as well as Alzheimer's Disease [7].…”

mentioning

confidence: 99%

Velocity-Based Signal Features for the Assessment of Parkinsonian Handwriting

Impedovo

2019

IEEE Signal Process. Lett.

View full text Add to dashboard Cite

show abstract

Characterizing Early-Stage Alzheimer Through Spatiotemporal Dynamics of Handwriting

Cited by 37 publications

References 19 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

Velocity-Based Signal Features for the Assessment of Parkinsonian Handwriting

Contact Info

Product

Resources

About