Implementation of an iPod wireless accelerometer application using machine learning to classify disparity of hemiplegic and healthy patellar tendon reflex pair.
Identifieur interne : 003538 ( PubMed/Corpus ); précédent : 003537; suivant : 003539Implementation of an iPod wireless accelerometer application using machine learning to classify disparity of hemiplegic and healthy patellar tendon reflex pair.
Auteurs : Robert Lemoyne ; Wesley T. Kerr ; Kevin Zanjani ; Timothy MastroianniSource :
- 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
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Kerr</name><affiliation><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></affiliation></author><author><name sortKey="Zanjani, Kevin" sort="Zanjani, Kevin" uniqKey="Zanjani K" first="Kevin" last="Zanjani">Kevin Zanjani</name><affiliation><nlm:affiliation>University of Southern California, Marshall School of Business, 3670 Trousdale Parkway, Los Angeles California 90089, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Mastroianni, Timothy" sort="Mastroianni, Timothy" uniqKey="Mastroianni T" first="Timothy" last="Mastroianni">Timothy Mastroianni</name><affiliation><nlm:affiliation>Independent, 793 Ella Street Pittsburgh, Pennsylvania 15243, USA.</nlm:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2014">2014</date><idno type="RBID">pubmed:25685611</idno><idno type="pmid">25685611</idno><idno type="doi">10.1166/jmihi.2014.1219</idno><idno type="wicri:Area/PubMed/Corpus">003538</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">003538</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Implementation of an iPod wireless accelerometer application using machine learning to classify disparity of hemiplegic and healthy patellar tendon reflex pair.</title><author><name sortKey="Lemoyne, Robert" sort="Lemoyne, Robert" uniqKey="Lemoyne R" first="Robert" last="Lemoyne">Robert Lemoyne</name><affiliation><nlm:affiliation>Department of Biological Sciences, Northern Arizona University, Flagstaff, Arizona 86011-5640 USA.</nlm:affiliation></affiliation></author><author><name sortKey="Kerr, Wesley T" sort="Kerr, Wesley T" uniqKey="Kerr W" first="Wesley T" last="Kerr">Wesley T. Kerr</name><affiliation><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></affiliation></author><author><name sortKey="Zanjani, Kevin" sort="Zanjani, Kevin" uniqKey="Zanjani K" first="Kevin" last="Zanjani">Kevin Zanjani</name><affiliation><nlm:affiliation>University of Southern California, Marshall School of Business, 3670 Trousdale Parkway, Los Angeles California 90089, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Mastroianni, Timothy" sort="Mastroianni, Timothy" uniqKey="Mastroianni T" first="Timothy" last="Mastroianni">Timothy Mastroianni</name><affiliation><nlm:affiliation>Independent, 793 Ella Street Pittsburgh, Pennsylvania 15243, USA.</nlm:affiliation></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><pubmed><MedlineCitation Status="Publisher" Owner="NLM"><PMID Version="1">25685611</PMID><DateCreated><Year>2015</Year><Month>02</Month><Day>16</Day></DateCreated><DateRevised><Year>2017</Year><Month>02</Month><Day>20</Day></DateRevised><Article PubModel="Print"><Journal><ISSN IssnType="Print">2156-7018</ISSN><JournalIssue CitedMedium="Print"><Volume>4</Volume><Issue>1</Issue><PubDate><Year>2014</Year><Month>Mar</Month></PubDate></JournalIssue><Title>Journal of medical imaging and health informatics</Title><ISOAbbreviation>J Med Imaging Health Inform</ISOAbbreviation></Journal><ArticleTitle>Implementation of an iPod wireless accelerometer application using machine learning to classify disparity of hemiplegic and healthy patellar tendon reflex pair.</ArticleTitle><Pagination><MedlinePgn>21-28</MedlinePgn></Pagination><Abstract><AbstractText>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.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>LeMoyne</LastName><ForeName>Robert</ForeName><Initials>R</Initials><AffiliationInfo><Affiliation>Department of Biological Sciences, Northern Arizona University, Flagstaff, Arizona 86011-5640 USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Kerr</LastName><ForeName>Wesley T</ForeName><Initials>WT</Initials><AffiliationInfo><Affiliation>David Geffen School of Medicine at the University of California, Los Angeles, 760 Westwood Plaza, Suite B8-169, Los Angeles, California 90095, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Zanjani</LastName><ForeName>Kevin</ForeName><Initials>K</Initials><AffiliationInfo><Affiliation>University of Southern California, Marshall School of Business, 3670 Trousdale Parkway, Los Angeles California 90089, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Mastroianni</LastName><ForeName>Timothy</ForeName><Initials>T</Initials><AffiliationInfo><Affiliation>Independent, 793 Ella Street Pittsburgh, Pennsylvania 15243, USA.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>R90 DA023422</GrantID><Acronym>DA</Acronym><Agency>NIDA NIH HHS</Agency><Country>United States</Country></Grant><Grant><GrantID>T32 GM008042</GrantID><Acronym>GM</Acronym><Agency>NIGMS NIH HHS</Agency><Country>United States</Country></Grant><Grant><GrantID>T32 GM008185</GrantID><Acronym>GM</Acronym><Agency>NIGMS NIH HHS</Agency><Country>United States</Country></Grant><Grant><GrantID>T90 DA022768</GrantID><Acronym>DA</Acronym><Agency>NIDA NIH HHS</Agency><Country>United States</Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>J Med Imaging Health Inform</MedlineTA><NlmUniqueID>101570358</NlmUniqueID><ISSNLinking>2156-7026</ISSNLinking></MedlineJournalInfo><CommentsCorrectionsList><CommentsCorrections RefType="Cites"><RefSource>Med Sci Sports Exerc. 1988 Apr;20(2):172-7</RefSource><PMID Version="1">3367753</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>J Neurol. 1990 Nov;237(7):427-31</RefSource><PMID Version="1">2273412</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Conf Proc IEEE Eng Med Biol Soc. 2013;2013:4658-61</RefSource><PMID Version="1">24110773</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>J Biomech. 2007;40(9):2107-11</RefSource><PMID Version="1">17140585</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Neurobiol Aging. 1989 Jul-Aug;10(4):311-5</RefSource><PMID 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