Implementation of an iPod wireless accelerometer application using machine learning to classify disparity of hemiplegic and healthy patellar tendon reflex pair.
Identifieur interne : 002820 ( Main/Exploration ); précédent : 002819; suivant : 002821Implementation of an iPod wireless accelerometer application using machine learning to classify disparity of hemiplegic and healthy patellar tendon reflex pair.
Auteurs : Robert Lemoyne [États-Unis] ; Wesley T. Kerr [États-Unis] ; Kevin Zanjani [États-Unis] ; Timothy Mastroianni [États-Unis]Source :
- Journal of medical imaging and health informatics [ 2156-7018 ] ; 2014.
Abstract
The characteristics of the patellar tendon reflex provide fundamental insight regarding the diagnosis of neurological status. Based on the features of the tendon reflex response, a clinician may establish preliminary perspective regarding the global condition of the nervous system. Current techniques for quantifying the observations of the reflex response involve the application of ordinal scales, requiring the expertise of a highly skilled clinician. However, the reliability of the ordinal scale approach is debatable. Highly skilled clinicians have even disputed the presence of asymmetric reflex pairs. An alternative strategy was the implementation of an iPod wireless accelerometer application to quantify the reflex response acceleration waveform. An application enabled the recording of the acceleration waveform and later wireless transmission as an email attachment by connectivity to the Internet. A potential energy impact pendulum enabled the patellar tendon reflex to be evoked in a predetermined and targeted manner. Three feature categories of the reflex response acceleration waveform (global parameters, temporal organization, and spectral features) were incorporated into machine learning to distinguish a subject's hemiplegic and healthy reflex pair. Machine learning attained perfect classification of the hemiplegic and healthy reflex pair. The research findings implicate the promise of machine learning for providing increased diagnostic acuity regarding the acceleration waveform of the tendon reflex response.
DOI: 10.1166/jmihi.2014.1219
PubMed: 25685611
Affiliations:
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Kerr</name><affiliation wicri:level="1"><nlm:affiliation>David Geffen School of Medicine at the University of California, Los Angeles, 760 Westwood Plaza, Suite B8-169, Los Angeles, California 90095, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>David Geffen School of Medicine at the University of California, Los Angeles, 760 Westwood Plaza, Suite B8-169, Los Angeles, California 90095</wicri:regionArea><wicri:noRegion>California 90095</wicri:noRegion></affiliation></author><author><name sortKey="Zanjani, Kevin" sort="Zanjani, Kevin" uniqKey="Zanjani K" first="Kevin" last="Zanjani">Kevin Zanjani</name><affiliation wicri:level="4"><nlm:affiliation>University of Southern California, Marshall School of Business, 3670 Trousdale Parkway, Los Angeles California 90089, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>University of Southern California, Marshall School of Business, 3670 Trousdale Parkway, Los Angeles California 90089</wicri:regionArea><orgName type="university">Université de Californie du Sud</orgName><placeName><settlement type="city">Los Angeles</settlement><region type="state">Californie</region></placeName></affiliation></author><author><name sortKey="Mastroianni, Timothy" sort="Mastroianni, Timothy" uniqKey="Mastroianni T" first="Timothy" last="Mastroianni">Timothy Mastroianni</name><affiliation wicri:level="1"><nlm:affiliation>Independent, 793 Ella Street Pittsburgh, Pennsylvania 15243, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Independent, 793 Ella Street Pittsburgh, Pennsylvania 15243</wicri:regionArea><wicri:noRegion>Pennsylvania 15243</wicri:noRegion></affiliation></author></analytic><series><title level="j">Journal of medical imaging and health informatics</title><idno type="ISSN">2156-7018</idno><imprint><date when="2014" type="published">2014</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">The characteristics of the patellar tendon reflex provide fundamental insight regarding the diagnosis of neurological status. Based on the features of the tendon reflex response, a clinician may establish preliminary perspective regarding the global condition of the nervous system. Current techniques for quantifying the observations of the reflex response involve the application of ordinal scales, requiring the expertise of a highly skilled clinician. However, the reliability of the ordinal scale approach is debatable. Highly skilled clinicians have even disputed the presence of asymmetric reflex pairs. An alternative strategy was the implementation of an iPod wireless accelerometer application to quantify the reflex response acceleration waveform. An application enabled the recording of the acceleration waveform and later wireless transmission as an email attachment by connectivity to the Internet. A potential energy impact pendulum enabled the patellar tendon reflex to be evoked in a predetermined and targeted manner. Three feature categories of the reflex response acceleration waveform (global parameters, temporal organization, and spectral features) were incorporated into machine learning to distinguish a subject's hemiplegic and healthy reflex pair. Machine learning attained perfect classification of the hemiplegic and healthy reflex pair. The research findings implicate the promise of machine learning for providing increased diagnostic acuity regarding the acceleration waveform of the tendon reflex response.</div></front></TEI><affiliations><list><country><li>États-Unis</li></country><region><li>Arizona</li><li>Californie</li></region><settlement><li>Los Angeles</li></settlement><orgName><li>Université de Californie du Sud</li></orgName></list><tree><country name="États-Unis"><region name="Arizona"><name sortKey="Lemoyne, Robert" sort="Lemoyne, Robert" uniqKey="Lemoyne R" first="Robert" last="Lemoyne">Robert Lemoyne</name></region><name sortKey="Kerr, Wesley T" sort="Kerr, Wesley T" uniqKey="Kerr W" first="Wesley T" last="Kerr">Wesley T. Kerr</name><name sortKey="Mastroianni, Timothy" sort="Mastroianni, Timothy" uniqKey="Mastroianni T" first="Timothy" last="Mastroianni">Timothy Mastroianni</name><name sortKey="Zanjani, Kevin" sort="Zanjani, Kevin" uniqKey="Zanjani K" first="Kevin" last="Zanjani">Kevin Zanjani</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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