Conference paper Open Access

Early Parkinson's Disease Detection via Touchscreen Typing Analysis using Convolutional Neural Networks

Dimitrios Iakovakis; Stelios Hadjidimitriou; Vasileios Charisis; Sevasti Bostanjopoulou; Zoe Katsarou; Lisa Klingelhoefer; Simone Mayer; Heinz Reichmann; Sofia B. Dias; José A. Diniz; Dhaval Trivedi; Ray K. Chaudhuri; Leontios J. Hadjileontiadis


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  <identifier identifierType="DOI">10.5281/zenodo.3675381</identifier>
  <creators>
    <creator>
      <creatorName>Dimitrios Iakovakis</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-6854-5942</nameIdentifier>
      <affiliation>Aristotle University of Thessaloniki</affiliation>
    </creator>
    <creator>
      <creatorName>Stelios Hadjidimitriou</creatorName>
      <affiliation>Aristotle University of Thessaloniki</affiliation>
    </creator>
    <creator>
      <creatorName>Vasileios Charisis</creatorName>
      <affiliation>Aristotle University of Thessaloniki</affiliation>
    </creator>
    <creator>
      <creatorName>Sevasti Bostanjopoulou</creatorName>
      <affiliation>Department of Neurology, Hippokration Hospital</affiliation>
    </creator>
    <creator>
      <creatorName>Zoe Katsarou</creatorName>
      <affiliation>Department of Neurology, Hippokration Hospital Thessaloniki</affiliation>
    </creator>
    <creator>
      <creatorName>Lisa Klingelhoefer</creatorName>
      <affiliation>Department of Neurology Technical University Dresden</affiliation>
    </creator>
    <creator>
      <creatorName>Simone Mayer</creatorName>
      <affiliation>Department of Neurology Technical University Dresden</affiliation>
    </creator>
    <creator>
      <creatorName>Heinz Reichmann</creatorName>
      <affiliation>Department of Neurology Technical University Dresden</affiliation>
    </creator>
    <creator>
      <creatorName>Sofia B. Dias</creatorName>
      <affiliation>Faculdade de Motricidade Humana Universidade de Lisboa</affiliation>
    </creator>
    <creator>
      <creatorName>José A. Diniz</creatorName>
      <affiliation>Faculdade de Motricidade Humana Universidade de Lisboa</affiliation>
    </creator>
    <creator>
      <creatorName>Dhaval Trivedi</creatorName>
      <affiliation>International Parkinson Excellence Research Centre, King's College Hospital NHS Foundation Trust, London, United Kingdom</affiliation>
    </creator>
    <creator>
      <creatorName>Ray K. Chaudhuri</creatorName>
      <affiliation>International Parkinson Excellence Research Centre, King's College Hospital NHS Foundation Trust, London, United Kingdom</affiliation>
    </creator>
    <creator>
      <creatorName>Leontios J. Hadjileontiadis</creatorName>
      <affiliation>Aristotle University of Thessaloniki</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Early Parkinson's Disease Detection via Touchscreen Typing Analysis using Convolutional Neural Networks</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2019</publicationYear>
  <dates>
    <date dateType="Issued">2019-07-28</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3675381</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.3675380</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;Parkinson&amp;rsquo;s Disease (PD) is the second most&amp;nbsp;common neurodegenerative disorder worldwide, causing both&amp;nbsp;motor and non-motor&amp;nbsp; symptoms. In the early stages, symptoms&amp;nbsp;are mild and patients may ignore their existence. As a result,&amp;nbsp;they do not undergo any related clinical examination; hence delaying their PD diagnosis. In an effort to remedy such delay,&amp;nbsp;analysis of data passively&amp;nbsp; captured from user&amp;rsquo;s interaction with&amp;nbsp;consumer technologies has been recently explored towards&amp;nbsp;remote screening of early PD motor signs. In the current study,&amp;nbsp;a smartphone-based method analyzing subjects&amp;rsquo; finger&amp;nbsp;interaction with the smartphone screen is developed for the&amp;nbsp;quantification of fine-motor skills decline in early PD using&amp;nbsp;Convolutional Neural Networks. Experimental results from the&amp;nbsp;analysis of keystroke typing in-the-clinic data from 18 early PD&amp;nbsp;patients and 15 healthy controls have shown a classification&lt;br&gt;
performance of 0.89 Area Under the Curve (AUC) with 0.79/0.79&amp;nbsp;sensitivity/specificity, respectively. Evaluation of the&amp;nbsp;generalization ability of the proposed approach was made by its&amp;nbsp;application on typing data arising from a separate self-reported&amp;nbsp;cohort of 27 PD patients&amp;rsquo; and 84 healthy controls&amp;rsquo; daily usage&amp;nbsp;with their personal smartphones (data in-the-wild), achieving&amp;nbsp;0.79 AUC with 0.74/0.78 sensitivity/specificity, respectively. The&amp;nbsp;results show the potentiality of the proposed approach to process&amp;nbsp;keystroke dynamics arising from users&amp;rsquo; natural typing activity&amp;nbsp;to detect PD, which contributes to the development of digital&amp;nbsp;tools for remote pathological symptom screening.&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/690494/">690494</awardNumber>
      <awardTitle>Intelligent Parkinson eaRly detectiOn Guiding NOvel Supportive InterventionS</awardTitle>
    </fundingReference>
  </fundingReferences>
</resource>
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