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  <doc>
    <id>4757</id>
    <completedYear/>
    <publishedYear>2015</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>171</pageFirst>
    <pageLast>176</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace/>
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    <title language="eng">A Step Towards the Automated Diagnosis of Parkinson's Disease: Analyzing Handwriting Movements</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">2015 IEEE 28th International Symposium on Computer-Based Medical Systems, 22.-25.06.2015, Sao Carlos, Brazil</parentTitle>
    <identifier type="doi">10.1109/cbms.2015.34</identifier>
    <identifier type="issn">2372-9198</identifier>
    <identifier type="isbn">978-1-4673-6775-2</identifier>
    <enrichment key="opus.import.date">2022-07-06T16:27:45+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">importuser</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">false</enrichment>
    <author>Clayton R. Pereira</author>
    <author>Danilo R. Pereira</author>
    <author>Francisco A. da Silva</author>
    <author>Christian Hook</author>
    <author>Silke A. T. Weber</author>
    <author>Luis A. M. Pereira</author>
    <author>João Paulo Papa</author>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
  </doc>
  <doc>
    <id>3077</id>
    <completedYear/>
    <publishedYear>2016</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>79</pageFirst>
    <pageLast>88</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>136</volume>
    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
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    <title language="eng">A new computer vision-based approach to aid the diagnosis of Parkinson's disease</title>
    <abstract language="eng">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.&#13;
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.&#13;
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.&#13;
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.</abstract>
    <parentTitle language="eng">Computer Methods and Programs in Biomedicine</parentTitle>
    <identifier type="doi">10.1016/j.cmpb.2016.08.005</identifier>
    <enrichment key="opus.import.date">2022-03-17T05:53:40+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
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    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
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    <enrichment key="opus.urn.autoCreate">false</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Clayton R. Pereira</author>
    <author>Danilo R. Pereira</author>
    <author>Francisco A. Silva</author>
    <author>Joao P. Masieiro</author>
    <author>Silke A. T. Weber</author>
    <author>Christian Hook</author>
    <author>João Paulo Papa</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>CLASSIFICATION</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Micrography</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Parkinson's disease</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Pattern recognition</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>SPEECH</value>
    </subject>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
  </doc>
</export-example>
