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Parkinson’s disease (PD) is a progressive movement disorder characterized by tremors at rest, bradykinesia, and stifness. The alteration of handwriting (HW) faculties is one of the earliest motor symptoms in PD patients. This characteristic can be exploited to develop an automatic aid system for early detection of this pathologie. This article aims to assess the importance of diacritics and punctuation marks (DPM) in the PD patients and healthy controls (HCs) discrimination problem, by comparing the classification results obtained from three components: text carrying DPM, text without DPM, as well as only DPM. This work includes the Arabic manuscripts of 31 PD patients and 31 HCs. Furthermore, kinematic, mechanic, and inclination features were calculated for each component. Then, Adaboost models have been constructed on different feature sets, as well as on reduced sets formed in incremental manner using mRMR ranked-feature selection method. From the obtained results, it was concluded that the separating power of HW features in the classification problem of PD patients and HCs is present in all components of the Arabic text, but in varying degrees of importance. Despite the simple graphical nature of DPM, they are carrying of relevant diagnostic information, and effectively contributing to the improvement of PD detection performance. The highest accuracy of 93.54% was achieved for this component.

Parkinson’s disease (PD) is one of the most common neurodegenerative diseases affecting a large population worldwide. Parkinson’s disease is characterized by rigidity, slowness of movement and tremors at rest, these syndromes are frequently manifested in the deterioration of handwriting. The aim of this article is to perform online Arabic handwriting analysis for two types of tasks, TASK 1: copying arabic imposed text and TASK 2: writing arabic desired text. A novel method of handwriting selection features is proposed to obtain the relevant features to efficiently identify subjects with PD, based on Recursive Feature Elimination with Cross-Validation (RFECV), three different RFE estimators were compared: Support Vector Machine, Decision Trees and Random Forest, the selected features have been fed to the same classifiers above to determine the best classifier for predicting Parkinson’s disease. Result: An accuracy of 94.4% was obtained using SVM with Linear kernel, based on 55 features selected using RFE-SVM(Linear) for TASK 1, for TASK 2 an accuracy of 93.7% was obtained using SVM with RBF kernel, based only in 7 features selected using RFE-SVM(Linear). For all the classifiers used, this technique experimentally demonstrates an increase in performance metrics.

Introduction L’ecriture est un processus cognitif de haut niveau qui resulte de l’interaction de plusieurs facteurs du developpement cognitif, linguistique, et psychomoteur. La deterioration de celle-ci est un signe de dysfonctionnement dans l’une des spheres cognitives. Objectifs Notre objectif est de developper une technique innovante, permettant d’aider les professionnels de sante a depister une deterioration cognitive a un stade precoce lors des maladies neurodegeneratives a l’aide de l’analyse de l’ecriture manuscrite. Patients et methodes L’ecriture manuscrite est capturee sur tablette graphique (WACOM) et est analysee « en ligne » comme une sequence de signaux acquis (pression, vitesse, inclinaison du stylo) chez 18 marocains atteints de maladie d’Alzheimer. Elle est comparee a 18 sujets controles apparies selon l’âge et le niveau d’etudes. Nous avons utilise des statistiques descriptives en appliquant le test de Mann–Whitney U test qui est un test statistique non parametrique. Resultats Les resultats revelent des differences significatives entre le groupe controle et le groupe Alzheimer leger et modere. Les patients Alzheimer ont une vitesse et une acceleration plus faible, avec des temps en air plus eleves que les sujets controles. Discussion Nos resultats ont montre des differences significatives entre les groupes (MA leger/majeur vs SC) dans 4 mesures cinematiques (acceleration, vitesse, temps en air, temps sur tablette). Ils se rapprochent de ceux obtenus par l’etude de Werner et al. (2006) qui ont analyse l’ecriture en hebreu chez des patients Alzheimer, MCI et sujets controles et ont trouve un « temps en air » plus eleve chez leurs groupes de patient MA par rapport au SC. Conclusion Cette analyse preliminaire des resultats nous a permis de degager des caracteristiques discriminantes via l’analyse de differents parametres de l’ecriture manuscrite visant a identifier les deux groupes etudies.

In this paper, we propose to characterize the on-line handwriting for the early detection of Parkinson’s disease. Thus, using kinematics, mechanical, and spatial features of handwriting, we are looking for the characterization of Parkinson’s disease. This paper describes the phase of the data acquisition which is currently carried out with in the Neurological department of UHC Hassan II of Fez. Following this paper, we have proposed an approach based on unsupervised learning techniques for analyzing on-line handwriting of 34 Parkinson’s disease patients and 34 Healthy Controls according to quantitative and qualitative features. Based on 230 computed features for each participant, our study has uncovered three different types of writers. The results show that the complications of fine motor abilities in Parkinson’s disease patients is especially characterized by a significant degradation in handwriting kinematic features.

Parkinson's disease (PD) is the second most common neurodegenerative disease affecting significant portion of elderly population. One of the most frequent hallmarks and usually also the first manifestation of PD is deterioration of handwriting. Since the diagnosis of Parkinson's disease is difficult, researchers have worked to develop a support tool based on algorithms to separate healthy controls from PD patients. Online handwriting analysis is one of the methods that can be used to diagnose PD.In this study, we aimed to analyze the Arabic Handwriting of 18 Parkinson's disease (PD) patients and 18 age matched healthy controls. We focused on copying an Arabic text task. For each participant we have calculated 528 features, and the purpose of this study is to find a subset of selected handwriting features suitable for efficiently identifying subjects with PD. The selected features were fed to a support vector machine classifier with RBF kernel, whose aim is to identify the subjects suffering from PD. Robustness of this classifier is measured with three performance matrices i.e., accuracy, sensitivity, and specificity. The obtained results show almost 80% overall classification accuracy.

Background Parkinson’s disease (PD) affects millions of people worldwide, and it is predicted that this pathology will gravely increase in the next few years. Unfortunately, there’s currently no cure for this disease, indeed an early diagnosis of Parkinson’s disease can help to better manage its symptoms and its evolution. One of the most frequent abilities and usually also the first manifestation of Parkinson’s disease is alteration of handwriting. New method We propose a novel method to detect Parkinson’s disease, based on the segmentation of the online handwritten text into lines. Indeed, we propose to compare Parkinson’s disease patients and healthy controls, based on the full dynamics of new temporal and spectral features. Three classifiers were used, K-Nearest Neighbors, Support Vector Machine and Decision Trees. The performances of these three classifiers were estimated using a stratified nested 10 cross-validation. All the models in this study have been evaluated using classification accuracy, balanced accuracy, sensitivity, specificity, F-Score and Matthews Correlation Coefficient. Results An accuracy of 92.86 % was obtained with Decision Trees classifier in the last line. The new categories of spectral and temporal features gave the best classification performances in comparison to the basic statistical features. Comparison with existing methods Previous studies have only focused on words or sentences. This is the first study to deal with the analysis of a text composed of several lines. Conclusion The last line discriminates at best between Parkinson’s disease patients and healthy controls. This obtained result has further strengthened our hypothesis concerning the fatigue occurring while writing in PD patients.

Parkinson's disease (PD) is the second most common neurodegenerative disease affecting significant portion of elderly population. One of the most frequent hallmarks and the first manifestation of PD is deterioration of handwriting. Since the diagnosis of Parkinson's disease is difficult, researchers have worked to develop a support tool based on algorithms to separate healthy controls from PD patients. On-line handwriting analysis is one of the methods that can be used to diagnose PD. In this study, we aimed to analyze the Arabic Handwriting of 28 Parkinson's disease patients and 28 healthy controls (HCs) who were the same age and have the same intellectual level. We focused on copying an Arabic text task. For each participant we have calculated 1482 features. Based on the most relevant features selected by the Pearson's coefficient correlation, the Hierarchical Ascendant Classification (HAC) was applied and generated 3 clusters of writers. The characterization of these clusters was carried out by using the quantitative and qualitative parameters. The obtained results show that the combination of these two aspects can discriminate at best PD patients from HCs.

International audience; We propose in this article to analyse on-line handwriting acquired on a digital graphic tablet for the early detection of neurological pathologies. Our main goal is the characterization of neurodegenerative diseases, such as Parkinson, Alzheimer, Mild Cognitive Impairment and neuropsychiatric diseases such as schizophrenia, through the use of spatiotemporal parameters of handwriting. This paper mainly describes the phase of handwriting and hand-drawn data acquisition currently underway at the Neurological Service at CHU Hassan II de Fez hospital, and gives a preliminary analysis of the spatiotemporal parameters extracted from the handwriting.; L'objectif de cet article est d'analyser l'écriture manuscrite en ligne, caractérisée par une intervention primordiale des muscles et du cerveau, afin de détecter des pathologies neurologiques comme l'Alzheimer et le Parkinson, qui sont liées à l'organisation de l'ensemble des facultés mentales. Dans ce cadre, une acquisition de données est réalisée au sein du service neurologie au CHU Hassan II de Fès. Elle est faite sur une tablette graphique WACOM permettant de récupérer les données spatiotemporelles de l'écriture manuscrite. La problématique de la détection de pathologies nécessite préalablement l'étude des paramètres descriptifs de l'écriture manuscrite permettant la caractérisation des sujets contrôles et des personnes atteintes. Cet article décrit principalement la phase d'acquisition de données en cours au CHU de Fès, ainsi que l'analyse préliminaire de paramètres spatiotemporels de l'écriture réalisée sur ces données

Introduction L’ecriture est un processus cognitif qui resulte de l’interaction entre plusieurs facteurs du developpement cognitifs, linguistiques, et psychomoteurs. De ce fait, la deterioration de celle-ci est un signe de dysfonctionnement dans l’une des spheres cognitives. Objectifs Notre objectif est de developper une technique innovante, permettant d’aider les professionnels de sante a depister une deterioration cognitive a un stade precoce lors des maladies neurodegeneratives a l’aide de l’analyse de l’ecriture manuscrite. Methodes L’ecriture manuscrite est capturee sur tablette graphique (WACOM) et est analysee « en ligne » comme une sequence de signaux acquis (position, pression, vitesse et inclinaison du stylo) chez des patients marocains atteints de maladie de Parkinson, maladie d’Alzheimer et MCI ; et sera comparer a des volontaires normaux. Resultats Nous avons effectue une premiere analyse des resultats de 34 parkinsoniens compares a 34 controles apparies selon l’âge et le niveau d’etudes. Les resultats revelent des differences significatives entre le groupe temoin et le groupe Parkinson, mais aussi dans le groupe Parkinson entre des patients de bas niveau et haut niveau socioculturel. Les parkinsoniens ont une vitesse et une acceleration plus faible, avec des hesitations plus elevees que les SC. Discussion Nous retrouvons des parametres discriminants de l’ecriture manuscrite permettant d’identifier des clusters de personnes ayant un vieillissement normal et d’autres souffrants de pathologies neurodegeneratives en fonction de leurs niveau socioculturel. Conclusion L’etude a permis dans un premier temps de determiner un ensemble caracterisant les malades de Parkinson a un stade precoce par rapport aux sujets sains.