Spatial and dynamical handwriting analysis in mild cognitive impairment

Kawa, Jacek; Bednorz, Adam; Stpie, Paula; Derejczyk, Jarosaw; Bugdol, Monika

doi:10.1016/j.compbiomed.2017.01.004

Cited by 39 publications

(28 citation statements)

References 30 publications

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“…MCI is characterized by slight problems with memory loss, language, or other mental functions; thus, finding deterministic patterns useful to discriminate the impairment from a non-pathological condition is very difficult: this is well-known to the research community working on neuroimaging. Some works, for example, References [40,104], clearly indicated that such difficulty is also reflected on dynamic handwriting analysis.…”

Section: Discussionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

“…Kawa et al [104] evaluated the usefulness of handwriting features obtained with an electronic pen to distinguish MCI patients from controls. Subjects with confirmed MCI needed more time to complete two out of three writing tasks, as their writing was significantly slower.…”

Section: Disease Diagnosismentioning

confidence: 99%

Dynamic Handwriting Analysis for Neurodegenerative Disease Assessment: A Literary Review

Vessio

2019

Applied Sciences

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“…It has been observed that AD patients, in order to proceed with the writing of a part of the word or of a new word, need to come back on the previously written one and to "rewrite" it, to some extent, in the air again. This aspect is important to evaluate patients with both the sequential programming engine and the competing processes altered [35]. Moreover, a sentence composed of more than one word allows the recording of the effect of fatigue during writing [16], [17].…”

Section: Writing Tasksmentioning

confidence: 99%

Dynamic Handwriting Analysis for the Assessment of Neurodegenerative Diseases: A Pattern Recognition Perspective

Impedovo

Pirlo

2019

IEEE Rev. Biomed. Eng.

111

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Neurodegenerative diseases, as for instance Alzheimer's Disease (AD) and Parkinson's Disease (PD), affect the peripheral nervous system, where nerve cells send the messages that control muscles in order to allow movements. Sick neurons cannot control muscles properly. Handwriting involves cognitive planning, coordination and execution abilities. Significant changes in the handwriting performance are a prominent feature of AD and PD. This work addresses the most relevant results obtained in the field of on-line (dynamic) analysis of handwritten trials by AD and PD patients. The survey is made from a pattern recognition point of view, so that different phases are described. Data acquisition deal not only with the device, but also with the handwriting task. Feature extraction can deal with function and parameter features. The classification problem is also discussed along with results already obtained. The paper also highlights the most profitable research direction.

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“…This capability augments the depth of insight that can be obtained from writing and drawing tasks and potentially unlocks a wealth of previously unmeasurable information. Research to date has focused on digitizing established drawing tasks for assessing cognitive impairment, including: signature writing [11,14], writing short phrases [15], pentagon copying [16][17][18], clock drawing [19], spiral drawing [20][21][22], circles and cursive looping [23,24]. Of these, writing words and phrases containing repetitions of cursive 'l' and 'e' have been commonly reported [25][26][27].…”

Section: Handwriting Analysis In Parkinson's Disease: An Overviewmentioning

confidence: 99%

Derivation and Analysis of Dynamic Handwriting Features as Clinical Markers of Parkinson’s Disease

Poon¹,

Gorji

Latt

et al. 2019

Proceedings of the Annual Hawaii International Conference on System Sciences

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Parkinson's Disease (PD) is a complex neurodegenerative disorder that is challenging to diagnose. Recent research has demonstrated predictive value in the analysis of dynamic handwriting features for detecting PD, however, consensus on clinicallyuseful features is yet to be reached. Here we explore and evaluate secondary kinematic handwriting features hypothesized to be diagnostically relevant to Parkinson's Disease using a publicly-available Spiral Drawing Test PD dataset. Univariate and multivariate analysis was performed on derived features. Classification outcome was determined using logistic regression models with 10-fold cross validation. Feature correlation was based on model specificity and sensitivity. Variations in grip angle, instantaneous acceleration and pressure indices were found to have high predictive potential as clinical markers of PD, with combined classification accuracy of above 90%. Our results show that the significance of secondary handwriting features and recommend the feature expansion step for hypothesis generation, comparative evaluation of test types and improved classification accuracy.

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Spatial and dynamical handwriting analysis in mild cognitive impairment

Cited by 39 publications

References 30 publications

Dynamic Handwriting Analysis for Neurodegenerative Disease Assessment: A Literary Review

Dynamic Handwriting Analysis for Neurodegenerative Disease Assessment: A Literary Review

Dynamic Handwriting Analysis for the Assessment of Neurodegenerative Diseases: A Pattern Recognition Perspective

Derivation and Analysis of Dynamic Handwriting Features as Clinical Markers of Parkinson’s Disease

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