Nonlinear dynamic analysis of oscillatory repetitive movements in Parkinson's disease and essential tremor
Identifieur interne : 001518 ( Main/Corpus ); précédent : 001517; suivant : 001519Nonlinear dynamic analysis of oscillatory repetitive movements in Parkinson's disease and essential tremor
Auteurs : João Costa ; Hernán A. González ; Francesc Valldeoriola ; Carles Gaig ; Eduardo Tolosa ; Josep Valls-SoléSource :
- Movement Disorders [ 0885-3185 ] ; 2010-11-15.
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- KwdEn :
Abstract
Uncertainty exists on whether Parkinson's disease (PD) and essential tremor (ET) patients have similar degree of impairment during motor tasks. We investigated this problem by analyzing nonlinear dynamics of repetitive movements in 21 control subjects, 33 mild‐moderate PD patients, and 18 ET patients. Accelerometer signals were recorded during finger tapping and unbounded forearm movements between two points, and processed with moving average filtering to generate a new signal consisting of the temporal distance between consecutive cycles. We calculated: mean interpeak interval (slowness), interpeak interval variability (irregularity), and beat decay (BD) of the auto mutual information (AMI) value, which estimates signal predictability by measuring the loss of signal information over a timescale. Both PD and ET had longer interpeak interval (except for finger tapping), higher interpeak interval variability, and higher BD‐AMI values than controls (P ≤ 0.007, all comparisons). ET patients had higher BD‐AMI values than PD (P = 0.003). BD‐AMI was the parameter that discriminated better between subjects (diagnosis accuracies about 80%). No differences existed between PD patients with and without tremor or between PD or ET patients with different disease stages, for any parameter. Evaluation of nonlinear dynamics of oscillatory repetitive movements is a feasible and promising tool for studying movement physiology. Movement performance is more predictable in PD and ET than in controls, even in early disease stages. Slowness and irregularity of movement in PD and ET cannot be fully explained by tremor. Some common pathogenic mechanisms leading to bradykinesia may contribute to this impairment. © 2010 Movement Disorder Society
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DOI: 10.1002/mds.23334
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We investigated this problem by analyzing nonlinear dynamics of repetitive movements in 21 control subjects, 33 mild‐moderate PD patients, and 18 ET patients. Accelerometer signals were recorded during finger tapping and unbounded forearm movements between two points, and processed with moving average filtering to generate a new signal consisting of the temporal distance between consecutive cycles. We calculated: mean interpeak interval (slowness), interpeak interval variability (irregularity), and beat decay (BD) of the auto mutual information (AMI) value, which estimates signal predictability by measuring the loss of signal information over a timescale. Both PD and ET had longer interpeak interval (except for finger tapping), higher interpeak interval variability, and higher BD‐AMI values than controls (P ≤ 0.007, all comparisons). ET patients had higher BD‐AMI values than PD (P = 0.003). BD‐AMI was the parameter that discriminated better between subjects (diagnosis accuracies about 80%). No differences existed between PD patients with and without tremor or between PD or ET patients with different disease stages, for any parameter. Evaluation of nonlinear dynamics of oscillatory repetitive movements is a feasible and promising tool for studying movement physiology. Movement performance is more predictable in PD and ET than in controls, even in early disease stages. Slowness and irregularity of movement in PD and ET cannot be fully explained by tremor. Some common pathogenic mechanisms leading to bradykinesia may contribute to this impairment. © 2010 Movement Disorder Society</div></front></TEI><istex><corpusName>wiley</corpusName><author><json:item><name>João Costa MD, PhD</name><affiliations><json:string>Institute of Molecular Medicine, Faculty of Medicine, University of Lisbon, Lisbon, Portugal</json:string><json:string>Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Spain</json:string></affiliations></json:item><json:item><name>Hernán A. González PhD</name><affiliations><json:string>Department of Mechanic Engineer, Polytechnic University of Catalunya (ETSEIB), Barcelona, Spain</json:string></affiliations></json:item><json:item><name>Francesc Valldeoriola MD, PhD</name><affiliations><json:string>Neurology Department, Hospital Clínic, Universitat de Barcelona, IDIBAPS (Institut d'Investigació Biomèdica August Pi i Sunyer), Spain</json:string></affiliations></json:item><json:item><name>Carles Gaig MD</name><affiliations><json:string>Neurology Department, Hospital Clínic, Universitat de Barcelona, IDIBAPS (Institut d'Investigació Biomèdica August Pi i Sunyer), Spain</json:string></affiliations></json:item><json:item><name>Eduardo Tolosa MD, PhD</name><affiliations><json:string>Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Spain</json:string><json:string>Neurology Department, Hospital Clínic, Universitat de Barcelona, IDIBAPS (Institut d'Investigació Biomèdica August Pi i Sunyer), Spain</json:string></affiliations></json:item><json:item><name>Josep Valls‐Solé MD, PhD</name><affiliations><json:string>Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Spain</json:string><json:string>EMG and Motor Control Unit, Neurology Department, Hospital Clínic, Universitat de Barcelona, IDIBAPS (Institut d'Investigació Biomèdica August Pi i Sunyer), Spain</json:string></affiliations></json:item></author><subject><json:item><lang><json:string>eng</json:string></lang><value>Parkinson's disease</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>essential tremor</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>neurophysiology</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>nonlinear dynamics</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>signal processing</value></json:item></subject><articleId><json:string>MDS23334</json:string></articleId><language><json:string>eng</json:string></language><abstract>Uncertainty exists on whether Parkinson's disease (PD) and essential tremor (ET) patients have similar degree of impairment during motor tasks. We investigated this problem by analyzing nonlinear dynamics of repetitive movements in 21 control subjects, 33 mild‐moderate PD patients, and 18 ET patients. Accelerometer signals were recorded during finger tapping and unbounded forearm movements between two points, and processed with moving average filtering to generate a new signal consisting of the temporal distance between consecutive cycles. We calculated: mean interpeak interval (slowness), interpeak interval variability (irregularity), and beat decay (BD) of the auto mutual information (AMI) value, which estimates signal predictability by measuring the loss of signal information over a timescale. Both PD and ET had longer interpeak interval (except for finger tapping), higher interpeak interval variability, and higher BD‐AMI values than controls (P ≤ 0.007, all comparisons). ET patients had higher BD‐AMI values than PD (P = 0.003). BD‐AMI was the parameter that discriminated better between subjects (diagnosis accuracies about 80%). No differences existed between PD patients with and without tremor or between PD or ET patients with different disease stages, for any parameter. Evaluation of nonlinear dynamics of oscillatory repetitive movements is a feasible and promising tool for studying movement physiology. Movement performance is more predictable in PD and ET than in controls, even in early disease stages. Slowness and irregularity of movement in PD and ET cannot be fully explained by tremor. 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We investigated this problem by analyzing nonlinear dynamics of repetitive movements in 21 control subjects, 33 mild‐moderate PD patients, and 18 ET patients. Accelerometer signals were recorded during finger tapping and unbounded forearm movements between two points, and processed with moving average filtering to generate a new signal consisting of the temporal distance between consecutive cycles. We calculated: mean interpeak interval (slowness), interpeak interval variability (irregularity), and beat decay (BD) of the auto mutual information (AMI) value, which estimates signal predictability by measuring the loss of signal information over a timescale. Both PD and ET had longer interpeak interval (except for finger tapping), higher interpeak interval variability, and higher BD‐AMI values than controls (P ≤ 0.007, all comparisons). ET patients had higher BD‐AMI values than PD (P = 0.003). BD‐AMI was the parameter that discriminated better between subjects (diagnosis accuracies about 80%). No differences existed between PD patients with and without tremor or between PD or ET patients with different disease stages, for any parameter. Evaluation of nonlinear dynamics of oscillatory repetitive movements is a feasible and promising tool for studying movement physiology. Movement performance is more predictable in PD and ET than in controls, even in early disease stages. Slowness and irregularity of movement in PD and ET cannot be fully explained by tremor. Some common pathogenic mechanisms leading to bradykinesia may contribute to this impairment. © 2010 Movement Disorder Society</p></abstract><textClass xml:lang="en"><keywords scheme="keyword"><list><head>Keywords</head><item><term>Parkinson's disease</term></item><item><term>essential tremor</term></item><item><term>neurophysiology</term></item><item><term>nonlinear dynamics</term></item><item><term>signal processing</term></item></list></keywords></textClass><textClass><keywords scheme="Journal Subject"><list><head>article category</head><item><term>Research Article</term></item></list></keywords></textClass></profileDesc><revisionDesc><change when="2010-02-24">Received</change><change when="2010-06-07">Registration</change><change when="2010-11-15">Published</change></revisionDesc></teiHeader></istex:fulltextTEI><json:item><original>false</original><mimetype>text/plain</mimetype><extension>txt</extension><uri>https://api.istex.fr/document/F5843EF3A4F555E7653B3564185517FB653FFF14/fulltext/txt</uri></json:item></fulltext><metadata><istex:metadataXml wicri:clean="Wiley, elements deleted: body"><istex:xmlDeclaration>version="1.0" encoding="UTF-8" standalone="yes"</istex:xmlDeclaration><istex:document><component version="2.0" type="serialArticle" xml:lang="en"><header><publicationMeta level="product"><publisherInfo><publisherName>Wiley Subscription Services, Inc., A Wiley Company</publisherName><publisherLoc>Hoboken</publisherLoc></publisherInfo><doi registered="yes">10.1002/(ISSN)1531-8257</doi><issn type="print">0885-3185</issn><issn type="electronic">1531-8257</issn><idGroup><id type="product" value="MDS"></id></idGroup><titleGroup><title type="main" xml:lang="en" sort="MOVEMENT DISORDERS">Movement Disorders</title><title type="short">Mov. 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Spain</correspondenceTo><linkGroup><link type="toTypesetVersion" href="file:MDS.MDS23334.pdf"></link></linkGroup></publicationMeta><contentMeta><countGroup><count type="figureTotal" number="2"></count><count type="tableTotal" number="4"></count><count type="referenceTotal" number="49"></count><count type="wordTotal" number="7451"></count></countGroup><titleGroup><title type="main" xml:lang="en">Nonlinear dynamic analysis of oscillatory repetitive movements in Parkinson's disease and essential tremor<link href="#fn1"></link></title><title type="short" xml:lang="en">Nonlinear Dynamics of Repetitive Movements</title></titleGroup><creators><creator xml:id="au1" creatorRole="author" affiliationRef="#af1 #af2"><personName><givenNames>João</givenNames><familyName>Costa</familyName><degrees>MD, PhD</degrees></personName></creator><creator xml:id="au2" creatorRole="author" affiliationRef="#af3"><personName><givenNames>Hernán A.</givenNames><familyName>González</familyName><degrees>PhD</degrees></personName></creator><creator xml:id="au3" creatorRole="author" affiliationRef="#af4"><personName><givenNames>Francesc</givenNames><familyName>Valldeoriola</familyName><degrees>MD, PhD</degrees></personName></creator><creator xml:id="au4" creatorRole="author" affiliationRef="#af4"><personName><givenNames>Carles</givenNames><familyName>Gaig</familyName><degrees>MD</degrees></personName></creator><creator xml:id="au5" creatorRole="author" affiliationRef="#af2 #af4"><personName><givenNames>Eduardo</givenNames><familyName>Tolosa</familyName><degrees>MD, PhD</degrees></personName></creator><creator xml:id="au6" creatorRole="author" affiliationRef="#af2 #af5" corresponding="yes"><personName><givenNames>Josep</givenNames><familyName>Valls‐Solé</familyName><degrees>MD, PhD</degrees></personName><contactDetails><email>jvalls@clinic.ub.es</email></contactDetails></creator></creators><affiliationGroup><affiliation xml:id="af1" countryCode="PT" type="organization"><unparsedAffiliation>Institute of Molecular Medicine, Faculty of Medicine, University of Lisbon, Lisbon, Portugal</unparsedAffiliation></affiliation><affiliation xml:id="af2" countryCode="ES" type="organization"><unparsedAffiliation>Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Spain</unparsedAffiliation></affiliation><affiliation xml:id="af3" countryCode="ES" type="organization"><unparsedAffiliation>Department of Mechanic Engineer, Polytechnic University of Catalunya (ETSEIB), Barcelona, Spain</unparsedAffiliation></affiliation><affiliation xml:id="af4" countryCode="ES" type="organization"><unparsedAffiliation>Neurology Department, Hospital Clínic, Universitat de Barcelona, IDIBAPS (Institut d'Investigació Biomèdica August Pi i Sunyer), Spain</unparsedAffiliation></affiliation><affiliation xml:id="af5" countryCode="ES" type="organization"><unparsedAffiliation>EMG and Motor Control Unit, Neurology Department, Hospital Clínic, Universitat de Barcelona, IDIBAPS (Institut d'Investigació Biomèdica August Pi i Sunyer), Spain</unparsedAffiliation></affiliation></affiliationGroup><keywordGroup xml:lang="en" type="author"><keyword xml:id="kwd1">Parkinson's disease</keyword><keyword xml:id="kwd2">essential tremor</keyword><keyword xml:id="kwd3">neurophysiology</keyword><keyword xml:id="kwd4">nonlinear dynamics</keyword><keyword xml:id="kwd5">signal processing</keyword></keywordGroup><fundingInfo><fundingAgency>Aircraft Medical (former Morpheus Medical)</fundingAgency></fundingInfo><supportingInformation><p> Additional Supporting Information may be found in the online version of this article. </p><supportingInfoItem><mediaResource alt="supporting information" href="urn-x:wiley:08853185:media:mds23334:MDS_23334_sm_suppinfo"></mediaResource><caption>Supporting information</caption></supportingInfoItem></supportingInformation><abstractGroup><abstract type="main" xml:lang="en"><title type="main">Abstract</title><p>Uncertainty exists on whether Parkinson's disease (PD) and essential tremor (ET) patients have similar degree of impairment during motor tasks. We investigated this problem by analyzing nonlinear dynamics of repetitive movements in 21 control subjects, 33 mild‐moderate PD patients, and 18 ET patients. Accelerometer signals were recorded during finger tapping and unbounded forearm movements between two points, and processed with moving average filtering to generate a new signal consisting of the temporal distance between consecutive cycles. We calculated: mean interpeak interval (slowness), interpeak interval variability (irregularity), and beat decay (BD) of the auto mutual information (AMI) value, which estimates signal predictability by measuring the loss of signal information over a timescale. Both PD and ET had longer interpeak interval (except for finger tapping), higher interpeak interval variability, and higher BD‐AMI values than controls (<i>P</i> ≤ 0.007, all comparisons). ET patients had higher BD‐AMI values than PD (<i>P</i> = 0.003). BD‐AMI was the parameter that discriminated better between subjects (diagnosis accuracies about 80%). No differences existed between PD patients with and without tremor or between PD or ET patients with different disease stages, for any parameter. Evaluation of nonlinear dynamics of oscillatory repetitive movements is a feasible and promising tool for studying movement physiology. Movement performance is more predictable in PD and ET than in controls, even in early disease stages. Slowness and irregularity of movement in PD and ET cannot be fully explained by tremor. Some common pathogenic mechanisms leading to bradykinesia may contribute to this impairment. © 2010 Movement Disorder Society</p></abstract></abstractGroup></contentMeta><noteGroup><note xml:id="fn1"><p>Potential conflict of interest: Nothing to report.</p></note></noteGroup></header></component></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Nonlinear dynamic analysis of oscillatory repetitive movements in Parkinson's disease and essential tremor</title></titleInfo><titleInfo type="abbreviated" lang="en"><title>Nonlinear Dynamics of Repetitive Movements</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Nonlinear dynamic analysis of oscillatory repetitive movements in Parkinson's disease and essential tremor</title></titleInfo><name type="personal"><namePart type="given">João</namePart><namePart type="family">Costa</namePart><namePart type="termsOfAddress">MD, PhD</namePart><affiliation>Institute of Molecular Medicine, Faculty of Medicine, University of Lisbon, Lisbon, Portugal</affiliation><affiliation>Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Spain</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Hernán A.</namePart><namePart type="family">González</namePart><namePart type="termsOfAddress">PhD</namePart><affiliation>Department of Mechanic Engineer, Polytechnic University of Catalunya (ETSEIB), Barcelona, Spain</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Francesc</namePart><namePart type="family">Valldeoriola</namePart><namePart type="termsOfAddress">MD, PhD</namePart><affiliation>Neurology Department, Hospital Clínic, Universitat de Barcelona, IDIBAPS (Institut d'Investigació Biomèdica August Pi i Sunyer), Spain</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Carles</namePart><namePart type="family">Gaig</namePart><namePart type="termsOfAddress">MD</namePart><affiliation>Neurology Department, Hospital Clínic, Universitat de Barcelona, IDIBAPS (Institut d'Investigació Biomèdica August Pi i Sunyer), Spain</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Eduardo</namePart><namePart type="family">Tolosa</namePart><namePart type="termsOfAddress">MD, PhD</namePart><affiliation>Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Spain</affiliation><affiliation>Neurology Department, Hospital Clínic, Universitat de Barcelona, IDIBAPS (Institut d'Investigació Biomèdica August Pi i Sunyer), Spain</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Josep</namePart><namePart type="family">Valls‐Solé</namePart><namePart type="termsOfAddress">MD, PhD</namePart><affiliation>Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Spain</affiliation><affiliation>EMG and Motor Control Unit, Neurology Department, Hospital Clínic, Universitat de Barcelona, IDIBAPS (Institut d'Investigació Biomèdica August Pi i Sunyer), Spain</affiliation><description>Correspondence: EMG Unit, Neurology Department, Hospital Clínic, Villarroel, 170, Barcelona 08036. Spain</description><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="article" displayLabel="article"></genre><originInfo><publisher>Wiley Subscription Services, Inc., A Wiley Company</publisher><place><placeTerm type="text">Hoboken</placeTerm></place><dateIssued encoding="w3cdtf">2010-11-15</dateIssued><dateCaptured encoding="w3cdtf">2010-02-24</dateCaptured><dateValid encoding="w3cdtf">2010-06-07</dateValid><copyrightDate encoding="w3cdtf">2010</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType><extent unit="figures">2</extent><extent unit="tables">4</extent><extent unit="references">49</extent><extent unit="words">7451</extent></physicalDescription><abstract lang="en">Uncertainty exists on whether Parkinson's disease (PD) and essential tremor (ET) patients have similar degree of impairment during motor tasks. We investigated this problem by analyzing nonlinear dynamics of repetitive movements in 21 control subjects, 33 mild‐moderate PD patients, and 18 ET patients. Accelerometer signals were recorded during finger tapping and unbounded forearm movements between two points, and processed with moving average filtering to generate a new signal consisting of the temporal distance between consecutive cycles. We calculated: mean interpeak interval (slowness), interpeak interval variability (irregularity), and beat decay (BD) of the auto mutual information (AMI) value, which estimates signal predictability by measuring the loss of signal information over a timescale. Both PD and ET had longer interpeak interval (except for finger tapping), higher interpeak interval variability, and higher BD‐AMI values than controls (P ≤ 0.007, all comparisons). ET patients had higher BD‐AMI values than PD (P = 0.003). BD‐AMI was the parameter that discriminated better between subjects (diagnosis accuracies about 80%). No differences existed between PD patients with and without tremor or between PD or ET patients with different disease stages, for any parameter. Evaluation of nonlinear dynamics of oscillatory repetitive movements is a feasible and promising tool for studying movement physiology. Movement performance is more predictable in PD and ET than in controls, even in early disease stages. Slowness and irregularity of movement in PD and ET cannot be fully explained by tremor. Some common pathogenic mechanisms leading to bradykinesia may contribute to this impairment. © 2010 Movement Disorder Society</abstract><note type="content">*Potential conflict of interest: Nothing to report.</note><note type="funding">Aircraft Medical (former Morpheus Medical)</note><subject lang="en"><genre>Keywords</genre><topic>Parkinson's disease</topic><topic>essential tremor</topic><topic>neurophysiology</topic><topic>nonlinear dynamics</topic><topic>signal processing</topic></subject><relatedItem type="host"><titleInfo><title>Movement Disorders</title></titleInfo><titleInfo type="abbreviated"><title>Mov. Disord.</title></titleInfo><genre type="Journal">journal</genre><note type="content"> Additional Supporting Information may be found in the online version of this article.Supporting Info Item: Supporting information - </note><subject><genre>article category</genre><topic>Research Article</topic></subject><identifier type="ISSN">0885-3185</identifier><identifier type="eISSN">1531-8257</identifier><identifier type="DOI">10.1002/(ISSN)1531-8257</identifier><identifier type="PublisherID">MDS</identifier><part><date>2010</date><detail type="volume"><caption>vol.</caption><number>25</number></detail><detail type="issue"><caption>no.</caption><number>15</number></detail><extent unit="pages"><start>2577</start><end>2586</end><total>10</total></extent></part></relatedItem><identifier type="istex">F5843EF3A4F555E7653B3564185517FB653FFF14</identifier><identifier type="DOI">10.1002/mds.23334</identifier><identifier type="ArticleID">MDS23334</identifier><accessCondition type="use and reproduction" contentType="copyright">Copyright © 2010 Movement Disorder Society</accessCondition><recordInfo><recordContentSource>WILEY</recordContentSource><recordOrigin>Wiley Subscription Services, Inc., A Wiley Company</recordOrigin></recordInfo></mods></metadata><serie></serie></istex></record>Pour manipuler ce document sous Unix (Dilib)
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