Intelligent Sensory Pen for Aiding in the Diagnosis of Parkinson’s Disease from Dynamic Handwriting Analysis (original) (raw)
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Biometric handwriting analysis to support Parkinson’s Disease assessment and grading
BMC Medical Informatics and Decision Making
Background Handwriting represents one of the major symptom in Parkinson’s Disease (PD) patients. The computer-aided analysis of the handwriting allows for the identification of promising patterns that might be useful in PD detection and rating. In this study, we propose an innovative set of features extracted by geometrical, dynamical and muscle activation signals acquired during handwriting tasks, and evaluate the contribution of such features in detecting and rating PD by means of artificial neural networks. Methods Eleven healthy subjects and twenty-one PD patients were enrolled in this study. Each involved subject was asked to write three different patterns on a graphic tablet while wearing the Myo Armband used to collect the muscle activation signals of the main forearm muscles. We have then extracted several features related to the written pattern, the movement of the pen and the pressure exerted with the pen and the muscle activations. The computed features have been used to ...
Contribution of different handwriting modalities to differential diagnosis of Parkinson's Disease
2015 IEEE International Symposium on Medical Measurements and Applications (MeMeA) Proceedings, 2015
In this paper, we evaluate the contribution of different handwriting modalities to the diagnosis of Parkinson's disease. We analyse on-surface movement, in-air movement and pressure exerted on the tablet surface. Especially in-air movement and pressure-based features have been rarely taken into account in previous studies. We show that pressure and in-air movement also possess information that is relevant for the diagnosis of Parkinson's Disease (PD) from handwriting. In addition to conventional kinematic and spatio-temporal features we present group of novel features based on entropy and empirical mode decomposition of the handwriting signal. The presented results indicate that handwriting can be used as biomarker for PD providing classification performance around 89% area under the ROC curve (AUC) for PD classification.
Exploiting spectral and cepstral handwriting features on diagnosing Parkinson’s disease
IEEE Access
Parkinson's disease (PD) is the second most frequent neurodegenerative disease associated with several motor symptoms, including alterations in handwriting, also known as PD dysgraphia. Several computerized decision support systems for PD dysgraphia have been proposed, however, the associated challenges require new approaches for more accurate diagnosis. Therefore, this work adds spectral and cepstral handwriting features to the already-used temporal, kinematic and statistics handwriting features. First, we calculate temporal and kinematic features using displacement; statistic features () using displacement, and horizontal and vertical displacement; spectral () and cepstral () using displacement, horizontal and vertical displacement and pressure. Since the employed dataset (PaHaW) contains only 37 PD patients and 38 healthy control subjects (HC), then as the second step, we augment the percentage of the smaller training set to equal the larger. Next, we augment both classes to increase the training patient's data and added random Gaussian noise in all augmentations. Third, the most relevant features were selected using the modified fast correlation-based filtering method (mFCBF). Finally, autoML is employed to train and test more than ten plain and ensembled classifiers. Experimental results show that adding spectral and cepstral features to temporal, kinematics and statistics features highly improved classification accuracy to 98.57%. Our proposed model, with lower computational complexities, outperforms conventional state-of-the-art models for all tasks, which is 97.62%.
Diagnosis of Parkinson Disease using Handwriting Analysis
International journal of computer applications, 2022
Parkinson is a neurodegenerative disease that affects your ability to control movement. Parkinson's disease starts slowly and worsens over time. The cured for Parkinson"s disease is still unknown; medications might significantly improve your symptoms. Researchers suggest that early diagnosis of Parkinson can help improve the quality of the patient"s life. In this survey, handwriting or drawings is considered as an aspect for detecting Parkinson disease using machine learning algorithm such as Random Forest Classifier and for detailed analysis of the drawings we use, Histogram of Oriented Gradients (HOG). We take drawings drawn by Parkinson patients as well as healthy people as input for detecting the Parkinson disease
A Step Towards the Automated Diagnosis of Parkinson's Disease: Analyzing Handwriting Movements
2015 IEEE 28th International Symposium on Computer-Based Medical Systems, 2015
The talk will describe elements of the system, the challenges in its design, the scientific advances, and also describe its impact. The talk will also briefly highlight a second project focusing on the malaria epidemic in parts of Asia and Africa, its treatment, and activities by NIH computational science researchers for developing systems to aid in medical research and clinical decision making.
Classification of handwriting patterns in patients with Parkinsons disease, using a biometric sensor
Parkinson disease (PD) is characterized by typical movement disorders, important for clinical diagnosis and management. Objective assessment may be possible by mathematic classification of characteristics extracted by a sensor BiSP (Biosensor smart pen). The study aim to analyze handwriting characteristics of PD patients using a biosensor, and to classify the results by SVM-Support Vector Machines. 36 PD patients (group I) and 48 healthy adults (control group) with similar demographic characteristics were included. All realized drawing of patterned figures (spirals and meander) and tested diadochokinesia (pronation-supination test), using the BiSP pen. Biometric data were obtained from pen pressure, finger pressure on pen tip, acceleration of the movement, dislocation, tremor and instability. For each sensor were extracted characteristic features. Classification was tested using 70% of the data for learning and 30% for testing for each group, using the mathematic model of support vector machines. Accuracy of correct classification for each group and figure was described. For each figure, 8 to 12 features were extracted and submitted to SVM classification. Correct classification of PD patients and controls showed an accuracy of 96.7% for spirals, 95.4% for meander, 92.5% for diadochokinesia of the dominant hand and 93.6% diadochokinesia of the nondominant hand. Combination of three figures, meander, spirales and diadochokinesia resulted in 99.6% of correct classification. The biometric features obtained by the BiSP permitted a correct classification of PD patients and control, using SMV as the mathematic tool. Biometrics and applied mathematics may help in PD characterization and follow-up.
Feature selection for an improved Parkinson's disease identification based on handwriting
2017 1st International Workshop on Arabic Script Analysis and Recognition (ASAR), 2017
Parkinson's disease (PD) is a neurological disorder associated with a progressive decline in motor skills, speech, and cognitive processes. Since the diagnosis of Parkinson's disease is difficult, researchers have worked to develop a support tool based on algorithms to differentiate healthy controls from PD patients. Online handwriting analysis is one of the methods that can be used to diagnose PD. The aim of this study is to find a subset of handwriting features suitable for efficiently identifying subjects with PD. Data was taken from PDMultiMC database collected in Lebanon, and consisting of 16 medicated PD patients and 16 age matched controls. Seven handwriting tasks were collected such as copying patterns, copying words in Arabic, and writing full names. For each task kinematic and spatio-temporal, pressure, energy, entropy, and intrinsic features were extracted. Feature selection was done in two stages, the first stage selected a subset using statistical analysis, and the second step select the most relevant features of this subset, by a suboptimal approach. The selected features were fed to a support vector machine classifier with RBF kernel, whose aim is to identify the subjects suffering from PD. The accuracy of the classification of PD was as high as 96.875%, with sensitivity and specificity equal to 93.75 % and 100%. The results as well as the selected features suggest that handwriting can be a valuable marker as a diagnosis tool.
Pattern Recognition Letters, 2019
Patients suffering from Parkinson's disease are characterized by an abnormal handwriting activity since they have difficulties in motor coordination and a decline in cognition. In this paper, we propose a model-free technique for differentiating Parkinson's disease patients from healthy subjects by using a handwriting analysis tool based on computer vision and surface ElectroMyoGraphy (sEMG) signal-processing techniques and an Artificial Intelligence-based classifier. Experimental tests have been conducted with both healthy and Parkinson's Disease patients using the proposed technique to address some specific research scientific questions regarding most representative features, best writing patterns, best AI-based classification approach between ANN optimal topology and SVM approaches in terms of both accuracy and repeatability of the results. Finally, the obtained results are reported and discussed to infer some important properties on writing patterns, classification approaches and the role of muscular activities on the handwriting analysis applied to neurodegenerative disease research.
Computer Methods and Programs in Biomedicine, 2019
Background and objectives: Parkinson's disease is a neurological disorder that affects the motor system producing lack of coordination, resting tremor, and rigidity. Impairments in handwriting are among the main symptoms of the disease. Handwriting analysis can help in supporting the diagnosis and in monitoring the progress of the disease. This paper aims to evaluate the importance of different groups of features to model handwriting deficits that appear due to Parkinson's disease; and how those features are able to discriminate between Parkinson's disease patients and healthy subjects. Methods: Features based on kinematic, geometrical and non-linear dynamics analyses were evaluated to classify Parkinson's disease and healthy subjects. Classifiers based on K-nearest neighbors, support vector machines, and random forest were considered. Results: Accuracies of up to 93.1% were obtained in the classification of patients and healthy control subjects. A relevance analysis of the features indicated that those related to speed, acceleration, and pressure are the most discriminant. The automatic classification of patients in different stages of the disease shows κ indexes between 0.36 and 0.44. Accuracies of up to 83.3% were obtained in a different dataset used only for validation purposes. Conclusions: The results confirmed the negative impact of aging in the classification process when we considered different groups of healthy subjects. In addition, the results reported with the separate validation set comprise a step towards the development of automated tools to support the diagnosis process in clinical practice.
Parkinson’s Disease Diagnosis using Spiral Test on Digital Tablets
International Journal of Advanced Computer Science and Applications
For a proper diagnosis, Parkinson's disease (PD) requires frequent visits to the doctor for physical tests, causing a huge burden on the patient. As PD impairs the handwriting ability, the handwriting pattern can be used as an indicator for PD diagnosis. More specifically, the Static Spiral Test (SST) and the Dynamic Spiral Test (DST), that consists in retracing spirals using digital pen. Such exam can be self-conducted by the patient, and thus it would be convenient and non-time-consuming for both the patient and the medical staff. In this project, we designed and implemented a system that automatically self-aiddiagnoses PD using SST and DST on digital tablets. The system includes two main components, image processing techniques to pre-process and extract the appropriate visual features and machine learning techniques to recognize PD automatically. The conducted experiment showed that the semi-local Edge Histogram Descriptor extracted from DST drawing, and conveyed to a Gaussian Kernel Support Vector Machine outperforms the other considered systems with an accuracy, specificity and sensitivity around 90%.