High-resolution tracking of motor disorders in Parkinson's disease during unconstrained activity.
Identifieur interne : 000981 ( PubMed/Checkpoint ); précédent : 000980; suivant : 000982High-resolution tracking of motor disorders in Parkinson's disease during unconstrained activity.
Auteurs : Serge H. Roy [États-Unis] ; Bryan T. Cole ; L Don Gilmore ; Carlo J. De Luca ; Cathi A. Thomas ; Marie M. Saint-Hilaire ; S Hamid NawabSource :
- Movement disorders : official journal of the Movement Disorder Society [ 1531-8257 ] ; 2013.
English descriptors
- KwdEn :
- Aged, Algorithms, Antiparkinson Agents (therapeutic use), Dose-Response Relationship, Drug, Dyskinesias (diagnosis), Dyskinesias (etiology), Electromyography, Female, Humans, Male, Middle Aged, Monitoring, Ambulatory, Movement (physiology), Muscle, Skeletal (physiopathology), Parkinson Disease (diagnosis), Parkinson Disease (drug therapy), Parkinson Disease (physiopathology), Sensitivity and Specificity, Severity of Illness Index, Signal Processing, Computer-Assisted, Tremor (diagnosis), Tremor (etiology), Video Recording.
- MESH :
- chemical , therapeutic use : Antiparkinson Agents.
- diagnosis : Dyskinesias, Parkinson Disease, Tremor.
- drug therapy : Parkinson Disease.
- etiology : Dyskinesias, Tremor.
- physiology : Movement.
- physiopathology : Muscle, Skeletal, Parkinson Disease.
- Aged, Algorithms, Dose-Response Relationship, Drug, Electromyography, Female, Humans, Male, Middle Aged, Monitoring, Ambulatory, Sensitivity and Specificity, Severity of Illness Index, Signal Processing, Computer-Assisted, Video Recording.
Abstract
Parkinson's disease (PD) can present with a variety of motor disorders that fluctuate throughout the day, making assessment a challenging task. Paper-based measurement tools can be burdensome to the patient and clinician and lack the temporal resolution needed to accurately and objectively track changes in motor symptom severity throughout the day. Wearable sensor-based systems that continuously monitor PD motor disorders may help to solve this problem, although critical shortcomings persist in identifying multiple disorders at high temporal resolution during unconstrained activity. The purpose of this study was to advance the current state of the art by (1) introducing hybrid sensor technology to concurrently acquire surface electromyographic (sEMG) and accelerometer data during unconstrained activity and (2) analyzing the data using dynamic neural network algorithms to capture the evolving temporal characteristics of the sensor data and improve motor disorder recognition of tremor and dyskinesia. Algorithms were trained (n=11 patients) and tested (n=8 patients; n=4 controls) to recognize tremor and dyskinesia at 1-second resolution based on sensor data features and expert annotation of video recording during 4-hour monitoring periods of unconstrained daily activity. The algorithms were able to make accurate distinctions between tremor, dyskinesia, and normal movement despite the presence of diverse voluntary activity. Motor disorder severity classifications averaged 94.9% sensitivity and 97.1% specificity based on 1 sensor per symptomatic limb. These initial findings indicate that new sensor technology and software algorithms can be effective in enhancing wearable sensor-based system performance for monitoring PD motor disorders during unconstrained activities.
DOI: 10.1002/mds.25391
PubMed: 23520058
Affiliations:
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Saint-Hilaire</name></author><author><name sortKey="Nawab, S Hamid" sort="Nawab, S Hamid" uniqKey="Nawab S" first="S Hamid" last="Nawab">S Hamid Nawab</name></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2013">2013</date><idno type="RBID">pubmed:23520058</idno><idno type="pmid">23520058</idno><idno type="doi">10.1002/mds.25391</idno><idno type="wicri:Area/PubMed/Corpus">000978</idno><idno type="wicri:Area/PubMed/Curation">000978</idno><idno type="wicri:Area/PubMed/Checkpoint">000981</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">High-resolution tracking of motor disorders in Parkinson's disease during unconstrained activity.</title><author><name sortKey="Roy, Serge H" sort="Roy, Serge H" uniqKey="Roy S" first="Serge H" last="Roy">Serge H. Roy</name><affiliation wicri:level="1"><nlm:affiliation>NeuroMuscular Research Center, Boston University, Boston, Massachusetts 02215, USA. sroy@bu.edu</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>NeuroMuscular Research Center, Boston University, Boston, Massachusetts 02215</wicri:regionArea><wicri:noRegion>Massachusetts 02215</wicri:noRegion></affiliation></author><author><name sortKey="Cole, Bryan T" sort="Cole, Bryan T" uniqKey="Cole B" first="Bryan T" last="Cole">Bryan T. Cole</name></author><author><name sortKey="Gilmore, L Don" sort="Gilmore, L Don" uniqKey="Gilmore L" first="L Don" last="Gilmore">L Don Gilmore</name></author><author><name sortKey="De Luca, Carlo J" sort="De Luca, Carlo J" uniqKey="De Luca C" first="Carlo J" last="De Luca">Carlo J. De Luca</name></author><author><name sortKey="Thomas, Cathi A" sort="Thomas, Cathi A" uniqKey="Thomas C" first="Cathi A" last="Thomas">Cathi A. Thomas</name></author><author><name sortKey="Saint Hilaire, Marie M" sort="Saint Hilaire, Marie M" uniqKey="Saint Hilaire M" first="Marie M" last="Saint-Hilaire">Marie M. Saint-Hilaire</name></author><author><name sortKey="Nawab, S Hamid" sort="Nawab, S Hamid" uniqKey="Nawab S" first="S Hamid" last="Nawab">S Hamid Nawab</name></author></analytic><series><title level="j">Movement disorders : official journal of the Movement Disorder Society</title><idno type="eISSN">1531-8257</idno><imprint><date when="2013" type="published">2013</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Aged</term><term>Algorithms</term><term>Antiparkinson Agents (therapeutic use)</term><term>Dose-Response Relationship, Drug</term><term>Dyskinesias (diagnosis)</term><term>Dyskinesias (etiology)</term><term>Electromyography</term><term>Female</term><term>Humans</term><term>Male</term><term>Middle Aged</term><term>Monitoring, Ambulatory</term><term>Movement (physiology)</term><term>Muscle, Skeletal (physiopathology)</term><term>Parkinson Disease (diagnosis)</term><term>Parkinson Disease (drug therapy)</term><term>Parkinson Disease (physiopathology)</term><term>Sensitivity and Specificity</term><term>Severity of Illness Index</term><term>Signal Processing, Computer-Assisted</term><term>Tremor (diagnosis)</term><term>Tremor (etiology)</term><term>Video Recording</term></keywords><keywords scheme="MESH" type="chemical" qualifier="therapeutic use" xml:lang="en"><term>Antiparkinson Agents</term></keywords><keywords scheme="MESH" qualifier="diagnosis" xml:lang="en"><term>Dyskinesias</term><term>Parkinson Disease</term><term>Tremor</term></keywords><keywords scheme="MESH" qualifier="drug therapy" xml:lang="en"><term>Parkinson Disease</term></keywords><keywords scheme="MESH" qualifier="etiology" xml:lang="en"><term>Dyskinesias</term><term>Tremor</term></keywords><keywords scheme="MESH" qualifier="physiology" xml:lang="en"><term>Movement</term></keywords><keywords scheme="MESH" qualifier="physiopathology" xml:lang="en"><term>Muscle, Skeletal</term><term>Parkinson Disease</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Aged</term><term>Algorithms</term><term>Dose-Response Relationship, Drug</term><term>Electromyography</term><term>Female</term><term>Humans</term><term>Male</term><term>Middle Aged</term><term>Monitoring, Ambulatory</term><term>Sensitivity and Specificity</term><term>Severity of Illness Index</term><term>Signal Processing, Computer-Assisted</term><term>Video Recording</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Parkinson's disease (PD) can present with a variety of motor disorders that fluctuate throughout the day, making assessment a challenging task. Paper-based measurement tools can be burdensome to the patient and clinician and lack the temporal resolution needed to accurately and objectively track changes in motor symptom severity throughout the day. Wearable sensor-based systems that continuously monitor PD motor disorders may help to solve this problem, although critical shortcomings persist in identifying multiple disorders at high temporal resolution during unconstrained activity. The purpose of this study was to advance the current state of the art by (1) introducing hybrid sensor technology to concurrently acquire surface electromyographic (sEMG) and accelerometer data during unconstrained activity and (2) analyzing the data using dynamic neural network algorithms to capture the evolving temporal characteristics of the sensor data and improve motor disorder recognition of tremor and dyskinesia. Algorithms were trained (n=11 patients) and tested (n=8 patients; n=4 controls) to recognize tremor and dyskinesia at 1-second resolution based on sensor data features and expert annotation of video recording during 4-hour monitoring periods of unconstrained daily activity. The algorithms were able to make accurate distinctions between tremor, dyskinesia, and normal movement despite the presence of diverse voluntary activity. Motor disorder severity classifications averaged 94.9% sensitivity and 97.1% specificity based on 1 sensor per symptomatic limb. These initial findings indicate that new sensor technology and software algorithms can be effective in enhancing wearable sensor-based system performance for monitoring PD motor disorders during unconstrained activities.</div></front></TEI><pubmed><MedlineCitation Owner="NLM" Status="MEDLINE"><PMID Version="1">23520058</PMID><DateCreated><Year>2013</Year><Month>08</Month><Day>13</Day></DateCreated><DateCompleted><Year>2014</Year><Month>03</Month><Day>06</Day></DateCompleted><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1531-8257</ISSN><JournalIssue CitedMedium="Internet"><Volume>28</Volume><Issue>8</Issue><PubDate><Year>2013</Year><Month>Jul</Month></PubDate></JournalIssue><Title>Movement disorders : official journal of the Movement Disorder Society</Title><ISOAbbreviation>Mov. Disord.</ISOAbbreviation></Journal><ArticleTitle>High-resolution tracking of motor disorders in Parkinson's disease during unconstrained activity.</ArticleTitle><Pagination><MedlinePgn>1080-7</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1002/mds.25391</ELocationID><Abstract><AbstractText>Parkinson's disease (PD) can present with a variety of motor disorders that fluctuate throughout the day, making assessment a challenging task. Paper-based measurement tools can be burdensome to the patient and clinician and lack the temporal resolution needed to accurately and objectively track changes in motor symptom severity throughout the day. Wearable sensor-based systems that continuously monitor PD motor disorders may help to solve this problem, although critical shortcomings persist in identifying multiple disorders at high temporal resolution during unconstrained activity. The purpose of this study was to advance the current state of the art by (1) introducing hybrid sensor technology to concurrently acquire surface electromyographic (sEMG) and accelerometer data during unconstrained activity and (2) analyzing the data using dynamic neural network algorithms to capture the evolving temporal characteristics of the sensor data and improve motor disorder recognition of tremor and dyskinesia. Algorithms were trained (n=11 patients) and tested (n=8 patients; n=4 controls) to recognize tremor and dyskinesia at 1-second resolution based on sensor data features and expert annotation of video recording during 4-hour monitoring periods of unconstrained daily activity. The algorithms were able to make accurate distinctions between tremor, dyskinesia, and normal movement despite the presence of diverse voluntary activity. Motor disorder severity classifications averaged 94.9% sensitivity and 97.1% specificity based on 1 sensor per symptomatic limb. These initial findings indicate that new sensor technology and software algorithms can be effective in enhancing wearable sensor-based system performance for monitoring PD motor disorders during unconstrained activities.</AbstractText><CopyrightInformation>Copyright © 2013 Movement Disorder Society.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Roy</LastName><ForeName>Serge H</ForeName><Initials>SH</Initials><AffiliationInfo><Affiliation>NeuroMuscular Research Center, Boston University, Boston, Massachusetts 02215, USA. sroy@bu.edu</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Cole</LastName><ForeName>Bryan T</ForeName><Initials>BT</Initials></Author><Author ValidYN="Y"><LastName>Gilmore</LastName><ForeName>L Don</ForeName><Initials>LD</Initials></Author><Author ValidYN="Y"><LastName>De Luca</LastName><ForeName>Carlo J</ForeName><Initials>CJ</Initials></Author><Author ValidYN="Y"><LastName>Thomas</LastName><ForeName>Cathi A</ForeName><Initials>CA</Initials></Author><Author ValidYN="Y"><LastName>Saint-Hilaire</LastName><ForeName>Marie M</ForeName><Initials>MM</Initials></Author><Author ValidYN="Y"><LastName>Nawab</LastName><ForeName>S Hamid</ForeName><Initials>SH</Initials></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>EB007163</GrantID><Acronym>EB</Acronym><Agency>NIBIB NIH HHS</Agency><Country>United States</Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D052061">Research Support, N.I.H., Extramural</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2013</Year><Month>03</Month><Day>20</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>Mov Disord</MedlineTA><NlmUniqueID>8610688</NlmUniqueID><ISSNLinking>0885-3185</ISSNLinking></MedlineJournalInfo><ChemicalList><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D000978">Antiparkinson Agents</NameOfSubstance></Chemical></ChemicalList><CitationSubset>IM</CitationSubset><CommentsCorrectionsList><CommentsCorrections RefType="CommentIn"><RefSource>Mov Disord. 2013 Jul;28(8):1025-6</RefSource><PMID Version="1">23609557</PMID></CommentsCorrections><CommentsCorrections RefType="CommentIn"><RefSource>Mov Disord. 2013 Jul;28(8):1027-9</RefSource><PMID Version="1">23868561</PMID></CommentsCorrections></CommentsCorrectionsList><MeshHeadingList><MeshHeading><DescriptorName MajorTopicYN="N" UI="D000368">Aged</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D000465">Algorithms</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D000978">Antiparkinson Agents</DescriptorName><QualifierName MajorTopicYN="N" UI="Q000627">therapeutic use</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D004305">Dose-Response Relationship, Drug</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D020820">Dyskinesias</DescriptorName><QualifierName MajorTopicYN="Y" UI="Q000175">diagnosis</QualifierName><QualifierName MajorTopicYN="N" UI="Q000209">etiology</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D004576">Electromyography</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D005260">Female</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D006801">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D008297">Male</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D008875">Middle Aged</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D018670">Monitoring, Ambulatory</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D009068">Movement</DescriptorName><QualifierName MajorTopicYN="Y" UI="Q000502">physiology</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D018482">Muscle, Skeletal</DescriptorName><QualifierName MajorTopicYN="N" UI="Q000503">physiopathology</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D010300">Parkinson Disease</DescriptorName><QualifierName MajorTopicYN="Y" UI="Q000175">diagnosis</QualifierName><QualifierName MajorTopicYN="N" UI="Q000188">drug therapy</QualifierName><QualifierName MajorTopicYN="Y" UI="Q000503">physiopathology</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D012680">Sensitivity and Specificity</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D012720">Severity of Illness Index</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D012815">Signal Processing, Computer-Assisted</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D014202">Tremor</DescriptorName><QualifierName MajorTopicYN="Y" UI="Q000175">diagnosis</QualifierName><QualifierName MajorTopicYN="N" UI="Q000209">etiology</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D014741">Video Recording</DescriptorName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">EMG</Keyword><Keyword MajorTopicYN="N">Parkinson's disease quantification</Keyword><Keyword MajorTopicYN="N">accelerometer</Keyword><Keyword MajorTopicYN="N">dyskinesia</Keyword><Keyword MajorTopicYN="N">motor disorder</Keyword><Keyword MajorTopicYN="N">tremor</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2012</Year><Month>3</Month><Day>16</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2013</Year><Month>1</Month><Day>7</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2013</Year><Month>1</Month><Day>15</Day></PubMedPubDate><PubMedPubDate PubStatus="aheadofprint"><Year>2013</Year><Month>3</Month><Day>20</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2013</Year><Month>3</Month><Day>23</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2013</Year><Month>3</Month><Day>23</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2014</Year><Month>3</Month><Day>7</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">23520058</ArticleId><ArticleId IdType="doi">10.1002/mds.25391</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list><country><li>États-Unis</li></country></list><tree><noCountry><name sortKey="Cole, Bryan T" sort="Cole, Bryan T" uniqKey="Cole B" first="Bryan T" last="Cole">Bryan T. Cole</name><name sortKey="De Luca, Carlo J" sort="De Luca, Carlo J" uniqKey="De Luca C" first="Carlo J" last="De Luca">Carlo J. De Luca</name><name sortKey="Gilmore, L Don" sort="Gilmore, L Don" uniqKey="Gilmore L" first="L Don" last="Gilmore">L Don Gilmore</name><name sortKey="Nawab, S Hamid" sort="Nawab, S Hamid" uniqKey="Nawab S" first="S Hamid" last="Nawab">S Hamid Nawab</name><name sortKey="Saint Hilaire, Marie M" sort="Saint Hilaire, Marie M" uniqKey="Saint Hilaire M" first="Marie M" last="Saint-Hilaire">Marie M. Saint-Hilaire</name><name sortKey="Thomas, Cathi A" sort="Thomas, Cathi A" uniqKey="Thomas C" first="Cathi A" last="Thomas">Cathi A. Thomas</name></noCountry><country name="États-Unis"><noRegion><name sortKey="Roy, Serge H" sort="Roy, Serge H" uniqKey="Roy S" first="Serge H" last="Roy">Serge H. Roy</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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