Handwriting Analysis to Support Alzheimer’s Disease Diagnosis: A Preliminary Study

Cilia, Nicole Dalia; Stefano, Claudio De; Fontanella, Francesco; Molinara, Mario

doi:10.1007/978-3-030-29891-3_13

Cited by 27 publications

(13 citation statements)

References 16 publications

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“…For instance, Cilia and colleagues extracted a set of informative features, such as velocity, acceleration and jerk, from temporal data of the bi-dimensional coordinates of the handwriting (indicated as X and Y coordinates in the following) and the pen pressure. They used those features to classify AD patients [8]. Feature extraction, however, can be an extremely hard task.…”

Section: Vectorial Gp For Alzheimer's Disease Prediction Through Hand...mentioning

confidence: 99%

“…In this work, we explore the use of VE GP to classify patients affected by AD, on the basis of their ability to accomplish nine well-defined task related to handwriting. Those tasks were introduced in [8], and are described in Table 1, later in this paper. The participants involved in the study performed the required ac-tions on a graphic tablet, that acquires data in the form of X and Y coordinates and pressure.…”

Section: Vectorial Gp For Alzheimer's Disease Prediction Through Hand...mentioning

confidence: 99%

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Vectorial GP for Alzheimer’s Disease Prediction Through Handwriting Analysis

Azzali

Cilia

Stefano

et al. 2022

Lecture Notes in Computer Science

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Alzheimer's Disease (AD) is a neurodegenerative disease which causes a continuous cognitive decline. This decline has a strong impact on daily life of the people affected and on that of their relatives. Unfortunately, to date there is no cure for this disease. However, its early diagnosis helps to better manage the course of the disease with the treatments currently available. In recent years, AI researchers have become increasingly interested in developing tools for early diagnosis of AD based on handwriting analysis. In most cases, they use a feature engineering approach: domain knowledge by clinicians is used to define the set of features to extract from the raw data. In this paper, we present a novel approach based on vectorial genetic programming (VE GP) to recognize the handwriting of AD patients. VE GP is a recently defined method that enhances Genetic Programming (GP) and is able to directly manage time series in such a way to automatically extract informative features, without any need of human intervention. We applied VE GP to handwriting data in the form of time series consisting of spatial coordinates and pressure. These time series represent pen movements collected from people while performing handwriting tasks. The presented experimental results indicate that the proposed approach is effective for this type of application. Furthermore, VE GP is also able to generate rather small and simple models, that can be read and possibly interpreted. These models are reported and discussed in the last part of the paper.

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Section: Vectorial Gp For Alzheimer's Disease Prediction Through Hand...mentioning

confidence: 99%

Section: Vectorial Gp For Alzheimer's Disease Prediction Through Hand...mentioning

confidence: 99%

Vectorial GP for Alzheimer’s Disease Prediction Through Handwriting Analysis

Azzali

Cilia

Stefano

et al. 2022

Lecture Notes in Computer Science

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“…Copy six words (regular, non regular, non words) in the appropriate boxes 19 Copy the fields of a postal order 22 Copy a telephone number 25 Copy a paragraph 26 copy the word "pane" in the appropriate boxe 27 copy the word "mela" in the appropriate boxe 28 copy the word "prosciutto" in the appropriate boxe 29 copy the word "ciliegia" in the appropriate boxe 30 copy the word "lonfo" in the appropriate boxe 31 copy the word "taganaccio" in the appropriate boxe Note that task #17, in which the person is asked to write six different words, has been used in two different ways: in the first, we averaged feature values over the entire set of words. In the second, we averaged feature values over each single word: this means that we split task #17 into six further tasks (from #26 to #31).…”

Section: The Acquisition Protocolmentioning

confidence: 99%

“…From the data we extracted both dynamic and static features such as, for example, the velocity or the total length of the ink trace. In [15], [19] we presented some preliminary results, in which we used a subset of both features and tasks under consideration in the study presented here.…”

Section: Introductionmentioning

confidence: 99%

Feature Selection as a Tool to Support the Diagnosis of Cognitive Impairments Through Handwriting Analysis

2021

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Cognitive Impairments are cognitive deficits that are greater than expected for a person of a given age and level of education, but which do not significantly interfere with the daily life of the people affected. They range from mild to severe and are seen as a risk factor for Alzheimer's disease, currently the most common neurodegenerative brain disorder worldwide. In a previous study, we presented an experimental protocol comprising different handwriting tasks to be carried out by patients and a healthy control group: the aim was to investigate whether the analysis of the handwriting could be used as a tool to support the diagnosis of this kind of impairment. In the study presented here, we used a wellknown and widely-used feature selection approach to determine the most effective features for predicting the symptoms related to cognitive impairments via handwriting analysis. Our intention is to deepen the knowledge about the different cognitive functions affected by the onset of these diseases, as well as to improve the performance of the tools developed to support their diagnosis. The results showed that different sets of highly discriminant features, closely related to the cognitive skills impaired, were selected for the handwriting tasks making up the protocol, thus supporting our hypothesis that their use can be very helpful to support the diagnosis of cognitive impairment. INDEX TERMSMedical expert systems, Cognitive impairments, Feature selection.

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“…Writing is a complex human activity that requires a complex mixture of cognitive, kinesthetic, and perceptual-motor components ( Hunter,1974 ; Kverno, 2022 ), which includes visual and kinesthetic perception, motor planning, eye-hand coordination, visuomotor integration, dexterity, and manual skills ( Tseng and Cermak, 1993 ). Also, writing is one of the abilities affected by Alzheimer’s disease ( Platel, 1993 ; Tseng and Cermak, 1993 ; Hughes et al, 1997 ; Forbes et al, 2004 ; Caterina Silveri et al, 2007 ; Cilia et al, 2019 ). Indeed, when signs of the disease become apparent, the impairment may already have been significant and irreversible.…”

Section: Introductionmentioning

confidence: 99%

A study of auxiliary screening for Alzheimer’s disease based on handwriting characteristics

Zhang²,

Wei³

et al. 2023

Front. Aging Neurosci.

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Background and objectivesAlzheimer’s disease (AD) has an insidious onset, the early stages are easily overlooked, and there are no reliable, rapid, and inexpensive ancillary detection methods. This study analyzes the differences in handwriting kinematic characteristics between AD patients and normal elderly people to model handwriting characteristics. The aim is to investigate whether handwriting analysis has a promising future in AD auxiliary screening or even auxiliary diagnosis and to provide a basis for developing a handwriting-based diagnostic tool.Materials and methodsThirty-four AD patients (15 males, 77.15 ± 1.796 years) and 45 healthy controls (20 males, 74.78 ± 2.193 years) were recruited. Participants performed four writing tasks with digital dot-matrix pens which simultaneously captured their handwriting as they wrote. The writing tasks consisted of two graphics tasks and two textual tasks. The two graphics tasks are connecting fixed dots (task 1) and copying intersecting pentagons (task 2), and the two textual tasks are dictating three words (task 3) and copying a sentence (task 4). The data were analyzed by using Student’s t-test and Mann–Whitney U test to obtain statistically significant handwriting characteristics. Moreover, seven classification algorithms, such as eXtreme Gradient Boosting (XGB) and Logistic Regression (LR) were used to build classification models. Finally, the Receiver Operating Characteristic (ROC) curve, accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and Area Under Curve (AUC) were used to assess whether writing scores and kinematics parameters are diagnostic.ResultsKinematic analysis showed statistically significant differences between the AD and controlled groups for most parameters (p < 0.05, p < 0.01). The results found that patients with AD showed slower writing speed, tremendous writing pressure, and poorer writing stability. We built statistically significant features into a classification model, among which the model built by XGB was the most effective with a maximum accuracy of 96.55%. The handwriting characteristics also achieved good diagnostic value in the ROC analysis. Task 2 had a better classification effect than task 1. ROC curve analysis showed that the best threshold value was 0.084, accuracy = 96.30%, sensitivity = 100%, specificity = 93.41%, PPV = 92.21%, NPV = 100%, and AUC = 0.991. Task 4 had a better classification effect than task 3. ROC curve analysis showed that the best threshold value was 0.597, accuracy = 96.55%, sensitivity = 94.20%, specificity = 98.37%, PPV = 97.81%, NPV = 95.63%, and AUC = 0.994.ConclusionThis study’s results prove that handwriting characteristic analysis is promising in auxiliary AD screening or AD diagnosis.

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Handwriting Analysis to Support Alzheimer’s Disease Diagnosis: A Preliminary Study

Cited by 27 publications

References 16 publications

Vectorial GP for Alzheimer’s Disease Prediction Through Handwriting Analysis

Vectorial GP for Alzheimer’s Disease Prediction Through Handwriting Analysis

Feature Selection as a Tool to Support the Diagnosis of Cognitive Impairments Through Handwriting Analysis

A study of auxiliary screening for Alzheimer’s disease based on handwriting characteristics

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