Dynamically enhanced static handwriting representation for Parkinson’s disease detection

Diaz, Moises; Ferrer, Miguel A.; Impedovo, Donato; Pirlo, Giuseppe; Vessio, Gennaro

doi:10.1016/j.patrec.2019.08.018

Cited by 91 publications

(54 citation statements)

References 22 publications

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“…More recently, due to their increasing popularity in a plethora of recognition tasks, some works investigated the usefulness of deep learning approaches [25,55]. The features automatically extracted by a convolutional neural network can be used to feed a fully connected layer stacked on top of the convolutional base or a more classic statistical classifier.…”

Section: Discussionmentioning

confidence: 99%

“…Some tasks, in fact, may be redundant with other ones; others may even introduce noise in the data. Some recent works [24][25][26], in fact, employed ensembles of classifiers, each built on the feature space of every single task, emphasizing how a performance-driven selection of a subset of tasks can improve classification performance against the use of all tasks simultaneously. Generally speaking, handwriting tasks can be classified into simple drawing, simple writing, and complex tasks: they are described in the following paragraphs.…”

Section: Acquisition Protocolmentioning

confidence: 99%

“…It is worth remarking that, in all of the studies by Drotár et al, the spiral task was undertaken with no significant impact on classification. This may have been due to the use of measures only tailored to handwriting; instead, visual features, for example, those provided by deep learning algorithms [25,89], seem to overcome this issue.…”

Section: Disease Diagnosismentioning

confidence: 99%

“…Diaz et al [25] recently proposed a "dynamically enhanced" handwriting representation which consists of synthetically generated images obtained by exploiting simultaneously static and dynamic properties of handwriting. Specifically, they proposed a static representation that embeds dynamic information based on drawing the points of the samples, instead of linking them, so as to retain temporal/velocity information, and that adds pen-ups in the same way.…”

Section: Disease Diagnosismentioning

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%

Section: Acquisition Protocolmentioning

confidence: 99%

Section: Disease Diagnosismentioning

confidence: 99%

Section: Disease Diagnosismentioning

confidence: 99%

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Dynamic Handwriting Analysis for Neurodegenerative Disease Assessment: A Literary Review

Vessio

2019

Applied Sciences

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“…The concept of dimensionality reduction in PCA pitches its use in facial recognition, computer vision and image compression. It has also wide spectrum of applications in pattern identification of high dimensional data pertaining to the field of finance, datamining, bio-informatics and psychology [30][31][32][33].…”

Section: Principal Component Analysismentioning

confidence: 99%

Early Detection of Diabetic Retinopathy Using PCA-Firefly Based Deep Learning Model

et al. 2020

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Diabetic Retinopathy is a major cause of vision loss and blindness affecting millions of people across the globe. Although there are established screening methods - fluorescein angiography and optical coherence tomography for detection of the disease but in majority of the cases, the patients remain ignorant and fail to undertake such tests at an appropriate time. The early detection of the disease plays an extremely important role in preventing vision loss which is the consequence of diabetes mellitus remaining untreated among patients for a prolonged time period. Various machine learning and deep learning approaches have been implemented on diabetic retinopathy dataset for classification and prediction of the disease but majority of them have neglected the aspect of data pre-processing and dimensionality reduction, leading to biased results. The dataset used in the present study is a diabetes retinopathy dataset collected from the UCI machine learning repository. At its inceptions, the raw dataset is normalized using the Standardscalar technique and then Principal Component Analysis (PCA) is used to extract the most significant features in the dataset. Further, Firefly algorithm is implemented for dimensionality reduction. This reduced dataset is fed into a Deep Neural Network Model for classification. The results generated from the model is evaluated against the prevalent machine learning models and the results justify the superiority of the proposed model in terms of Accuracy, Precision, Recall, Sensitivity and Specificity.

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Performance-Driven Handwriting Task Selection for Parkinson’s Disease Classification

Angelillo

Impedovo

Pirlo

et al. 2019

Lecture Notes in Computer Science

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Dynamically enhanced static handwriting representation for Parkinson’s disease detection

Cited by 91 publications

References 22 publications

Dynamic Handwriting Analysis for Neurodegenerative Disease Assessment: A Literary Review

Dynamic Handwriting Analysis for Neurodegenerative Disease Assessment: A Literary Review

Early Detection of Diabetic Retinopathy Using PCA-Firefly Based Deep Learning Model

Performance-Driven Handwriting Task Selection for Parkinson’s Disease Classification

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