@inproceedings{PereiraPereiradaSilvaetal., author = {Pereira, Clayton R. and Pereira, Danilo R. and da Silva, Francisco A. and Hook, Christian and Weber, Silke A. T. and Pereira, Luis A. M. and Papa, Jo{\~a}o Paulo}, title = {A Step Towards the Automated Diagnosis of Parkinson's Disease: Analyzing Handwriting Movements}, series = {2015 IEEE 28th International Symposium on Computer-Based Medical Systems, 22.-25.06.2015, Sao Carlos, Brazil}, booktitle = {2015 IEEE 28th International Symposium on Computer-Based Medical Systems, 22.-25.06.2015, Sao Carlos, Brazil}, publisher = {IEEE}, isbn = {978-1-4673-6775-2}, issn = {2372-9198}, doi = {10.1109/cbms.2015.34}, pages = {171 -- 176}, abstract = {Parkinson's disease (PD) has affected millions of people world-wide, being its major problem the loss of movements and, consequently, the ability of working and locomotion. Although we can find several works that attempt at dealing with this problem out there, most of them make use of datasets composed by a few subjects only. In this work, we present some results toward the automated diagnosis of PD by means of computer vision-based techniques in a dataset composed by dozens of patients, which is one of the main contributions of this work. The dataset is part of a joint research project that aims at extracting both visual and signal-based information from healthy and PD patients in order to go forward the early diagnosis of PD patients. The dataset is composed by handwriting clinical exams that are analyzed by means of image processing and machine learning techniques, being the preliminary results encouraging and promising. Additionally, a new quantitative feature to measure the amount of tremor of an individual's handwritten trace called Mean Relative Tremor is also presented.}, language = {en} } @article{PereiraPereiraSilvaetal., author = {Pereira, Clayton R. and Pereira, Danilo R. and Silva, Francisco A. and Masieiro, Joao P. and Weber, Silke A. T. and Hook, Christian and Papa, Jo{\~a}o Paulo}, title = {A new computer vision-based approach to aid the diagnosis of Parkinson's disease}, series = {Computer Methods and Programs in Biomedicine}, volume = {136}, journal = {Computer Methods and Programs in Biomedicine}, publisher = {Elsevier}, doi = {10.1016/j.cmpb.2016.08.005}, pages = {79 -- 88}, abstract = {Background and Objective: Even today, pointing out an exam that can diagnose a patient with Parkinson's disease (PD) accurately enough is not an easy task. Although a number of techniques have been used in search for a more precise method, detecting such illness and measuring its level of severity early enough to postpone its side effects are not straightforward. In this work, after reviewing a considerable number of works, we conclude that only a few techniques address the problem of PD recognition by means of micrography using computer vision techniques. Therefore, we consider the problem of aiding automatic PD diagnosis by means of spirals and meanders filled out in forms, which are then compared with the template for feature extraction. Methods: In our work, both the template and the drawings are identified and separated automatically using image processing techniques, thus needing no user intervention. Since we have no registered images, the idea is to obtain a suitable representation of both template and drawings using the very same approach for all images in a fast and accurate approach. Results: The results have shown that we can obtain very reasonable recognition rates (around approximate to 67\%), with the most accurate class being the one represented by the patients, which outnumbered the control individuals in the proposed dataset. Conclusions: The proposed approach seemed to be suitable for aiding in automatic PD diagnosis by means of computer vision and machine learning techniques. Also, meander images play an important role, leading to higher accuracies than spiral images. We also observed that the main problem in detecting PD is the patients in the early stages, who can draw near-perfect objects, which are very similar to the ones made by control patients. (C) 2016 Elsevier Ireland Ltd. All rights reserved.}, language = {en} } @article{PereiraPereiraWeberetal., author = {Pereira, Clayton R. and Pereira, Danilo R. and Weber, Silke A. T. and Hook, Christian}, title = {A survey on computer-assisted Parkinson's Disease diagnosis}, series = {Artificial intelligence in medicine}, volume = {95}, journal = {Artificial intelligence in medicine}, number = {April}, publisher = {Elsevier}, doi = {10.1016/j.artmed.2018.08.007}, pages = {48 -- 63}, abstract = {Background and objective: In this work, we present a systematic review concerning the recent enabling technologies as a tool to the diagnosis, treatment and better quality of life of patients diagnosed with Parkinson's Disease (PD), as well as an analysis of future trends on new approaches to this end. Methods: In this review, we compile a number of works published at some well-established databases, such as Science Direct, IEEEXplore, PubMed, Plos One, Multidisciplinary Digital Publishing Institute (MDPI), Association for Computing Machinery (ACM), Springer and Hindawi Publishing Corporation. Each selected work has been carefully analyzed in order to identify its objective, methodology and results. Results: The review showed the majority of works make use of signal-based data, which are often acquired by means of sensors. Also, we have observed the increasing number of works that employ virtual reality and e-health monitoring systems to increase the life quality of PD patients. Despite the different approaches found in the literature, almost all of them make use of some sort of machine learning mechanism to aid the automatic PD diagnosis. Conclusions: The main focus of this survey is to consider computer-assisted diagnosis, and how effective they can be when handling the problem of PD identification. Also, the main contribution of this review is to consider very recent works only, mainly from 2015 and 2016.}, language = {en} } @article{PereiraPereiraRosaetal., author = {Pereira, Clayton R. and Pereira, Danilo R. and Rosa, Gustavo H. and Albuquerque, Victor Hugo C. and Weber, Silke A. T. and Hook, Christian and Papa, Jo{\~a}o Paulo}, title = {Handwritten dynamics assessment through convolutional neural networks}, series = {Artificial Intelligence in Medicine}, volume = {37}, journal = {Artificial Intelligence in Medicine}, number = {May}, publisher = {Elsevier}, doi = {10.1016/j.artmed.2018.04.001}, pages = {67 -- 77}, abstract = {Background and objective Parkinson's disease (PD) is considered a degenerative disorder that affects the motor system, which may cause tremors, micrography, and the freezing of gait. Although PD is related to the lack of dopamine, the triggering process of its development is not fully understood yet. Methods In this work, we introduce convolutional neural networks to learn features from images produced by handwritten dynamics, which capture different information during the individual's assessment. Additionally, we make available a dataset composed of images and signal-based data to foster the research related to computer-aided PD diagnosis. Results The proposed approach was compared against raw data and texture-based descriptors, showing suitable results, mainly in the context of early stage detection, with results nearly to 95\%. Conclusions The analysis of handwritten dynamics using deep learning techniques showed to be useful for automatic Parkinson's disease identification, as well as it can outperform handcrafted features.}, language = {en} } @incollection{PereiraPassosLopesetal., author = {Pereira, Clayton R. and Passos, Leandro A. and Lopes, Ricardo R. and Weber, Silke A. T. and Hook, Christian and Papa, Joao Paulo}, title = {Parkinson's Disease Identification Using Restricted Boltzmann Machines}, series = {Computer Analysis of Images and Patterns, 17th International Conference, CAIP 2017, Ystad, Sweden, August 22-24, 2017, Proceedings, Part II}, volume = {10425}, booktitle = {Computer Analysis of Images and Patterns, 17th International Conference, CAIP 2017, Ystad, Sweden, August 22-24, 2017, Proceedings, Part II}, publisher = {Springer}, isbn = {978-3-319-64697-8}, doi = {10.1007/978-3-319-64698-5_7}, pages = {70 -- 80}, abstract = {Currently, Parkinson's Disease (PD) has no cure or accurate diagnosis, reaching approximately 60, 000 new cases yearly and worldwide, being more often in the elderly population. Its main symptoms can not be easily uncorrelated with other illness, being way more difficult to be identified at the early stages. As such, computer-aided tools have been recently used to assist in this task, but the challenge in the automatic identification of Parkinson's Disease still persists. In order to cope with this problem, we propose to employ Restricted Boltzmann Machines (RBMs) to learn features in an unsupervised fashion by analyzing images from handwriting exams, which aim at assessing the writing skills of potential individuals. These are one of the main symptoms of PD-prone people, since such kind of ability ends up being severely affected. We show that RBMs can learn proper features that help supervised classifiers in the task of automatic identification of PD patients, as well as one can obtain a more compact representation of the exam for the sake of storage and computational load purposes.}, language = {en} } @article{AfonsoPereiraWeberetal., author = {Afonso, Luis Claudio Sugi and Pereira, Clayton R. and Weber, Silke A. T. and Hook, Christian and Falc{\~a}o, Alexandre X. and Papa, Joao Paulo}, title = {Hierarchical learning using deep optimum-path forest}, series = {Journal of Visual Communication and Image Representation}, volume = {71}, journal = {Journal of Visual Communication and Image Representation}, number = {August}, publisher = {Elsevier}, doi = {10.1016/j.jvcir.2020.102823}, abstract = {Bag-of-Visual Words (BoVW) and deep learning techniques have been widely used in several domains, which include computer-assisted medical diagnoses. In this work, we are interested in developing tools for the automatic identification of Parkinson's disease using machine learning and the concept of BoVW. The proposed approach concerns a hierarchical-based learning technique to design visual dictionaries through the Deep Optimum-Path Forest classifier. The proposed method was evaluated in six datasets derived from data collected from individuals when performing handwriting exams. Experimental results showed the potential of the technique, with robust achievements.}, language = {en} } @article{AfonsoRosaPereiraetal., author = {Afonso, Luis Claudio Sugi and Rosa, Gustavo H. and Pereira, Clayton R. and Weber, Silke A. T. and Hook, Christian and Albuquerque, Victor Hugo C. and Papa, Joao Paulo}, title = {A recurrence plot-based approach for Parkinson's disease identification}, series = {Future generation computer systems - The international journal of escience}, volume = {94}, journal = {Future generation computer systems - The international journal of escience}, number = {May}, publisher = {Elsevier}, doi = {10.1016/j.future.2018.11.054}, pages = {282 -- 292}, abstract = {Parkinson's disease (PD) is a neurodegenerative disease that affects millions of people worldwide, causing mental and mainly motor dysfunctions. The negative impact on the patient's daily routine has moved the science in search of new techniques that can reduce its negative effects and also identify the disease in individuals. One of the main motor characteristics of PD is the hand tremor faced by patients, which turns out to be a crucial information to be used towards a computer-aided diagnosis. In this context, we make use of handwriting dynamics data acquired from individuals when submitted to some tasks that measure abilities related to writing skills. This work proposes the application of recurrence plots to map the signals onto the image domain, which are further used to feed a Convolutional Neural Network for learning proper information that can help the automatic identification of PD. The proposed approach was assessed in a public dataset under several scenarios that comprise different combinations of deep-based architectures, image resolutions, and training set sizes. Experimental results showed significant accuracy improvement compared to our previous work with an average accuracy of over 87\%. Moreover, it was observed an improvement in accuracy concerning the classification of patients (i.e., mean recognition rates above to 90\%). The promising results showed the potential of the proposed approach towards the automatic identification of Parkinson's disease.}, language = {en} }