A novel automatic method for monitoring Tourette motor tics through a wearable device
Identifieur interne : 000F99 ( Istex/Corpus ); précédent : 000F98; suivant : 001000A novel automatic method for monitoring Tourette motor tics through a wearable device
Auteurs : Michel Bernabei ; Ezio Preatoni ; Martin Mendez ; Luca Piccini ; Mauro Porta ; Giuseppe AndreoniSource :
- Movement Disorders [ 0885-3185 ] ; 2010-09-15.
English descriptors
Abstract
The aim of this study was to propose a novel automatic method for quantifying motor‐tics caused by the Tourette Syndrome (TS). In this preliminary report, the feasibility of the monitoring process was tested over a series of standard clinical trials in a population of 12 subjects affected by TS. A wearable instrument with an embedded three‐axial accelerometer was used to detect and classify motor tics during standing and walking activities. An algorithm was devised to analyze acceleration data by: eliminating noise; detecting peaks connected to pathological events; and classifying intensity and frequency of motor tics into quantitative scores. These indexes were compared with the video‐based ones provided by expert clinicians, which were taken as the gold‐standard. Sensitivity, specificity, and accuracy of tic detection were estimated, and an agreement analysis was performed through the least square regression and the Bland‐Altman test. The tic recognition algorithm showed sensitivity = 80.8% ± 8.5% (mean ± SD), specificity = 75.8% ± 17.3%, and accuracy = 80.5% ± 12.2%. The agreement study showed that automatic detection tended to overestimate the number of tics occurred. Although, it appeared this may be a systematic error due to the different recognition principles of the wearable and video‐based systems. Furthermore, there was substantial concurrency with the gold‐standard in estimating the severity indexes. The proposed methodology gave promising performances in terms of automatic motor‐tics detection and classification in a standard clinical context. The system may provide physicians with a quantitative aid for TS assessment. Further developments will focus on the extension of its application to everyday long‐term monitoring out of clinical environments. © 2010 Movement Disorder Society
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DOI: 10.1002/mds.23188
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ISTEX:7EAADFFB3158F886B533F63379B80328F29653CDLe document en format XML
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In this preliminary report, the feasibility of the monitoring process was tested over a series of standard clinical trials in a population of 12 subjects affected by TS. A wearable instrument with an embedded three‐axial accelerometer was used to detect and classify motor tics during standing and walking activities. An algorithm was devised to analyze acceleration data by: eliminating noise; detecting peaks connected to pathological events; and classifying intensity and frequency of motor tics into quantitative scores. These indexes were compared with the video‐based ones provided by expert clinicians, which were taken as the gold‐standard. Sensitivity, specificity, and accuracy of tic detection were estimated, and an agreement analysis was performed through the least square regression and the Bland‐Altman test. The tic recognition algorithm showed sensitivity = 80.8% ± 8.5% (mean ± SD), specificity = 75.8% ± 17.3%, and accuracy = 80.5% ± 12.2%. The agreement study showed that automatic detection tended to overestimate the number of tics occurred. Although, it appeared this may be a systematic error due to the different recognition principles of the wearable and video‐based systems. Furthermore, there was substantial concurrency with the gold‐standard in estimating the severity indexes. The proposed methodology gave promising performances in terms of automatic motor‐tics detection and classification in a standard clinical context. The system may provide physicians with a quantitative aid for TS assessment. Further developments will focus on the extension of its application to everyday long‐term monitoring out of clinical environments. © 2010 Movement Disorder Society</div></front></TEI><istex><corpusName>wiley</corpusName><author><json:item><name>Michel Bernabei MSc</name><affiliations><json:string>Dipartimento di Bioingegneria, Politecnico di Milano, Milan, Italy</json:string><json:string>Dipartimento di Industrial Design, Arti, Comunicazione e Moda (INDACO), Politecnico di Milano, Milan, Italy</json:string></affiliations></json:item><json:item><name>Ezio Preatoni PhD</name><affiliations><json:string>Dipartimento di Bioingegneria, Politecnico di Milano, Milan, Italy</json:string><json:string>Dipartimento di Industrial Design, Arti, Comunicazione e Moda (INDACO), Politecnico di Milano, Milan, Italy</json:string></affiliations></json:item><json:item><name>Martin Mendez PhD</name><affiliations><json:string>Dipartimento di Bioingegneria, Politecnico di Milano, Milan, Italy</json:string><json:string>Facultad de Ciencias, Universidad Autónoma de San Luis Potosí, San Luis Potosí, Mexico</json:string></affiliations></json:item><json:item><name>Luca Piccini PhD</name><affiliations><json:string>Dipartimento di Industrial Design, Arti, Comunicazione e Moda (INDACO), Politecnico di Milano, Milan, Italy</json:string></affiliations></json:item><json:item><name>Mauro Porta MD</name><affiliations><json:string>Department of Neurology, Tourette Centre, IRCCS “Galeazzi,” Milan, Italy</json:string></affiliations></json:item><json:item><name>Giuseppe Andreoni PhD</name><affiliations><json:string>Dipartimento di Industrial Design, Arti, Comunicazione e Moda (INDACO), Politecnico di Milano, Milan, Italy</json:string></affiliations></json:item></author><subject><json:item><lang><json:string>eng</json:string></lang><value>Tourette syndrome</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>automatic tic detection</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>accelerometers</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>wearable monitoring</value></json:item></subject><language><json:string>eng</json:string></language><abstract>The aim of this study was to propose a novel automatic method for quantifying motor‐tics caused by the Tourette Syndrome (TS). In this preliminary report, the feasibility of the monitoring process was tested over a series of standard clinical trials in a population of 12 subjects affected by TS. A wearable instrument with an embedded three‐axial accelerometer was used to detect and classify motor tics during standing and walking activities. An algorithm was devised to analyze acceleration data by: eliminating noise; detecting peaks connected to pathological events; and classifying intensity and frequency of motor tics into quantitative scores. These indexes were compared with the video‐based ones provided by expert clinicians, which were taken as the gold‐standard. Sensitivity, specificity, and accuracy of tic detection were estimated, and an agreement analysis was performed through the least square regression and the Bland‐Altman test. The tic recognition algorithm showed sensitivity = 80.8% ± 8.5% (mean ± SD), specificity = 75.8% ± 17.3%, and accuracy = 80.5% ± 12.2%. The agreement study showed that automatic detection tended to overestimate the number of tics occurred. Although, it appeared this may be a systematic error due to the different recognition principles of the wearable and video‐based systems. Furthermore, there was substantial concurrency with the gold‐standard in estimating the severity indexes. The proposed methodology gave promising performances in terms of automatic motor‐tics detection and classification in a standard clinical context. The system may provide physicians with a quantitative aid for TS assessment. 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The agreement study showed that automatic detection tended to overestimate the number of tics occurred. Although, it appeared this may be a systematic error due to the different recognition principles of the wearable and video‐based systems. Furthermore, there was substantial concurrency with the gold‐standard in estimating the severity indexes. The proposed methodology gave promising performances in terms of automatic motor‐tics detection and classification in a standard clinical context. The system may provide physicians with a quantitative aid for TS assessment. 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xml:id="af1" countryCode="IT" type="organization"><unparsedAffiliation>Dipartimento di Bioingegneria, Politecnico di Milano, Milan, Italy</unparsedAffiliation></affiliation><affiliation xml:id="af2" countryCode="IT" type="organization"><unparsedAffiliation>Dipartimento di Industrial Design, Arti, Comunicazione e Moda (INDACO), Politecnico di Milano, Milan, Italy</unparsedAffiliation></affiliation><affiliation xml:id="af3" countryCode="MX" type="organization"><unparsedAffiliation>Facultad de Ciencias, Universidad Autónoma de San Luis Potosí, San Luis Potosí, Mexico</unparsedAffiliation></affiliation><affiliation xml:id="af4" countryCode="IT" type="organization"><unparsedAffiliation>Department of Neurology, Tourette Centre, IRCCS “Galeazzi,” Milan, Italy</unparsedAffiliation></affiliation></affiliationGroup><keywordGroup xml:lang="en" type="author"><keyword xml:id="kwd1">Tourette syndrome</keyword><keyword xml:id="kwd2">automatic tic detection</keyword><keyword xml:id="kwd3">accelerometers</keyword><keyword xml:id="kwd4">wearable monitoring</keyword></keywordGroup><abstractGroup><abstract type="main" xml:lang="en"><title type="main">Abstract</title><p>The aim of this study was to propose a novel automatic method for quantifying motor‐tics caused by the Tourette Syndrome (TS). In this preliminary report, the feasibility of the monitoring process was tested over a series of standard clinical trials in a population of 12 subjects affected by TS. A wearable instrument with an embedded three‐axial accelerometer was used to detect and classify motor tics during standing and walking activities. An algorithm was devised to analyze acceleration data by: eliminating noise; detecting peaks connected to pathological events; and classifying intensity and frequency of motor tics into quantitative scores. These indexes were compared with the video‐based ones provided by expert clinicians, which were taken as the gold‐standard. Sensitivity, specificity, and accuracy of tic detection were estimated, and an agreement analysis was performed through the least square regression and the Bland‐Altman test. The tic recognition algorithm showed sensitivity = 80.8% ± 8.5% (mean ± SD), specificity = 75.8% ± 17.3%, and accuracy = 80.5% ± 12.2%. The agreement study showed that automatic detection tended to overestimate the number of tics occurred. Although, it appeared this may be a systematic error due to the different recognition principles of the wearable and video‐based systems. Furthermore, there was substantial concurrency with the gold‐standard in estimating the severity indexes. The proposed methodology gave promising performances in terms of automatic motor‐tics detection and classification in a standard clinical context. The system may provide physicians with a quantitative aid for TS assessment. Further developments will focus on the extension of its application to everyday long‐term monitoring out of clinical environments. © 2010 Movement Disorder Society</p></abstract></abstractGroup></contentMeta><noteGroup><note xml:id="fn1"><p>Potential conflict of interest: None of the authors presents conflict of interests or has received financial support for carrying out the present research.</p></note></noteGroup></header></component></istex:document></istex:metadataXml><!--Version 0.6 générée le 3-12-2015--><mods version="3.6"><titleInfo lang="en"><title>A novel automatic method for monitoring Tourette motor tics through a wearable device</title></titleInfo><titleInfo type="abbreviated" lang="en"><title>A Novel Method for Monitoring Tourette Motor Tics</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>A novel automatic method for monitoring Tourette motor tics through a wearable device</title></titleInfo><name type="personal"><namePart type="given">Michel</namePart><namePart type="family">Bernabei</namePart><namePart type="termsOfAddress">MSc</namePart><affiliation>Dipartimento di Bioingegneria, Politecnico di Milano, Milan, Italy</affiliation><affiliation>Dipartimento di Industrial Design, Arti, Comunicazione e Moda (INDACO), Politecnico di Milano, Milan, Italy</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Ezio</namePart><namePart type="family">Preatoni</namePart><namePart type="termsOfAddress">PhD</namePart><affiliation>Dipartimento di Bioingegneria, Politecnico di Milano, Milan, Italy</affiliation><affiliation>Dipartimento di Industrial Design, Arti, Comunicazione e Moda (INDACO), Politecnico di Milano, Milan, Italy</affiliation><description>Correspondence: Dipartimento INDACO, Politecnico di Milano, Piazza Leonardo Da Vinci, 32 – 20133 Milano, Italy</description><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Martin</namePart><namePart type="family">Mendez</namePart><namePart type="termsOfAddress">PhD</namePart><affiliation>Dipartimento di Bioingegneria, Politecnico di Milano, Milan, Italy</affiliation><affiliation>Facultad de Ciencias, Universidad Autónoma de San Luis Potosí, San Luis Potosí, Mexico</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Luca</namePart><namePart type="family">Piccini</namePart><namePart type="termsOfAddress">PhD</namePart><affiliation>Dipartimento di Industrial Design, Arti, Comunicazione e Moda (INDACO), Politecnico di Milano, Milan, Italy</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Mauro</namePart><namePart type="family">Porta</namePart><namePart type="termsOfAddress">MD</namePart><affiliation>Department of Neurology, Tourette Centre, IRCCS “Galeazzi,” Milan, Italy</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Giuseppe</namePart><namePart type="family">Andreoni</namePart><namePart type="termsOfAddress">PhD</namePart><affiliation>Dipartimento di Industrial Design, Arti, Comunicazione e Moda (INDACO), Politecnico di Milano, Milan, Italy</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre authority="originalCategForm">article</genre><originInfo><publisher>Wiley Subscription Services, Inc., A Wiley Company</publisher><place><placeTerm type="text">Hoboken</placeTerm></place><dateIssued encoding="w3cdtf">2010-09-15</dateIssued><dateCaptured encoding="w3cdtf">2009-11-04</dateCaptured><dateValid encoding="w3cdtf">2010-03-23</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">1</extent><extent unit="tables">1</extent><extent unit="references">21</extent><extent unit="words">4127</extent></physicalDescription><abstract lang="en">The aim of this study was to propose a novel automatic method for quantifying motor‐tics caused by the Tourette Syndrome (TS). In this preliminary report, the feasibility of the monitoring process was tested over a series of standard clinical trials in a population of 12 subjects affected by TS. A wearable instrument with an embedded three‐axial accelerometer was used to detect and classify motor tics during standing and walking activities. An algorithm was devised to analyze acceleration data by: eliminating noise; detecting peaks connected to pathological events; and classifying intensity and frequency of motor tics into quantitative scores. These indexes were compared with the video‐based ones provided by expert clinicians, which were taken as the gold‐standard. Sensitivity, specificity, and accuracy of tic detection were estimated, and an agreement analysis was performed through the least square regression and the Bland‐Altman test. The tic recognition algorithm showed sensitivity = 80.8% ± 8.5% (mean ± SD), specificity = 75.8% ± 17.3%, and accuracy = 80.5% ± 12.2%. The agreement study showed that automatic detection tended to overestimate the number of tics occurred. Although, it appeared this may be a systematic error due to the different recognition principles of the wearable and video‐based systems. Furthermore, there was substantial concurrency with the gold‐standard in estimating the severity indexes. The proposed methodology gave promising performances in terms of automatic motor‐tics detection and classification in a standard clinical context. The system may provide physicians with a quantitative aid for TS assessment. Further developments will focus on the extension of its application to everyday long‐term monitoring out of clinical environments. © 2010 Movement Disorder Society</abstract><note type="content">*Potential conflict of interest: None of the authors presents conflict of interests or has received financial support for carrying out the present research.</note><subject lang="en"><genre>Keywords</genre><topic>Tourette syndrome</topic><topic>automatic tic detection</topic><topic>accelerometers</topic><topic>wearable monitoring</topic></subject><relatedItem type="host"><titleInfo><title>Movement Disorders</title></titleInfo><titleInfo type="abbreviated"><title>Mov. Disord.</title></titleInfo><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>12</number></detail><extent unit="pages"><start>1967</start><end>1972</end><total>6</total></extent></part></relatedItem><identifier type="istex">7EAADFFB3158F886B533F63379B80328F29653CD</identifier><identifier type="DOI">10.1002/mds.23188</identifier><identifier type="ArticleID">MDS23188</identifier><accessCondition type="use and reproduction" contentType="copyright">Published 2010 Wiley‐Liss, Inc.</accessCondition><recordInfo><recordOrigin>Wiley Subscription Services, Inc., A Wiley Company</recordOrigin><recordContentSource>WILEY</recordContentSource></recordInfo></mods></metadata><serie></serie></istex></record>Pour manipuler ce document sous Unix (Dilib)
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