A novel automatic method for monitoring Tourette motor tics through a wearable device.
Identifieur interne : 001673 ( PubMed/Corpus ); précédent : 001672; suivant : 001674A 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 : official journal of the Movement Disorder Society [ 1531-8257 ] ; 2010.
English descriptors
- KwdEn :
- Adolescent, Adult, Female, Humans, Male, Middle Aged, Monitoring, Ambulatory (instrumentation), Monitoring, Ambulatory (methods), Sensitivity and Specificity, Severity of Illness Index, Tics (diagnosis), Tics (physiopathology), Tourette Syndrome (diagnosis), Tourette Syndrome (physiopathology), Video Recording.
- MESH :
- diagnosis : Tics, Tourette Syndrome.
- instrumentation : Monitoring, Ambulatory.
- methods : Monitoring, Ambulatory.
- physiopathology : Tics, Tourette Syndrome.
- Adolescent, Adult, Female, Humans, Male, Middle Aged, Sensitivity and Specificity, Severity of Illness Index, Video Recording.
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.
DOI: 10.1002/mds.23188
PubMed: 20669298
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pubmed:20669298Le 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.</div></front></TEI><pubmed><MedlineCitation Owner="NLM" Status="MEDLINE"><PMID Version="1">20669298</PMID><DateCreated><Year>2010</Year><Month>09</Month><Day>14</Day></DateCreated><DateCompleted><Year>2010</Year><Month>12</Month><Day>28</Day></DateCompleted><Article PubModel="Print"><Journal><ISSN IssnType="Electronic">1531-8257</ISSN><JournalIssue CitedMedium="Internet"><Volume>25</Volume><Issue>12</Issue><PubDate><Year>2010</Year><Month>Sep</Month><Day>15</Day></PubDate></JournalIssue><Title>Movement disorders : official journal of the Movement Disorder Society</Title><ISOAbbreviation>Mov. Disord.</ISOAbbreviation></Journal><ArticleTitle>A novel automatic method for monitoring Tourette motor tics through a wearable device.</ArticleTitle><Pagination><MedlinePgn>1967-72</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1002/mds.23188</ELocationID><Abstract><AbstractText>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.</AbstractText><CopyrightInformation>© 2010 Movement Disorder Society.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Bernabei</LastName><ForeName>Michel</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>Dipartimento di Bioingegneria, Politecnico di Milano, Milan, Italy.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Preatoni</LastName><ForeName>Ezio</ForeName><Initials>E</Initials></Author><Author ValidYN="Y"><LastName>Mendez</LastName><ForeName>Martin</ForeName><Initials>M</Initials></Author><Author ValidYN="Y"><LastName>Piccini</LastName><ForeName>Luca</ForeName><Initials>L</Initials></Author><Author ValidYN="Y"><LastName>Porta</LastName><ForeName>Mauro</ForeName><Initials>M</Initials></Author><Author ValidYN="Y"><LastName>Andreoni</LastName><ForeName>Giuseppe</ForeName><Initials>G</Initials></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>Mov Disord</MedlineTA><NlmUniqueID>8610688</NlmUniqueID><ISSNLinking>0885-3185</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName MajorTopicYN="N" UI="D000293">Adolescent</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D000328">Adult</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><QualifierName MajorTopicYN="Y" UI="Q000295">instrumentation</QualifierName><QualifierName MajorTopicYN="N" UI="Q000379">methods</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="D020323">Tics</DescriptorName><QualifierName MajorTopicYN="Y" UI="Q000175">diagnosis</QualifierName><QualifierName MajorTopicYN="N" UI="Q000503">physiopathology</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D005879">Tourette Syndrome</DescriptorName><QualifierName MajorTopicYN="Y" UI="Q000175">diagnosis</QualifierName><QualifierName MajorTopicYN="N" UI="Q000503">physiopathology</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D014741">Video Recording</DescriptorName></MeshHeading></MeshHeadingList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2010</Year><Month>7</Month><Day>30</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2010</Year><Month>7</Month><Day>30</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2010</Year><Month>12</Month><Day>29</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="doi">10.1002/mds.23188</ArticleId><ArticleId IdType="pubmed">20669298</ArticleId></ArticleIdList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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