Ambulatory motor assessment in Parkinson's disease
Identifieur interne : 001360 ( Main/Exploration ); précédent : 001359; suivant : 001361Ambulatory motor assessment in Parkinson's disease
Auteurs : Noël L. W. Keijsers [Pays-Bas] ; Martin W. I. M. Horstink [Pays-Bas] ; Stan C. A. M. Gielen [Pays-Bas]Source :
- Movement Disorders [ 0885-3185 ] ; 2006-01.
English descriptors
Abstract
We developed an algorithm that distinguishes between on and off states in patients with Parkinson's disease during daily life activities. Twenty‐three patients were monitored continuously in a home‐like situation for approximately 3 hours while they carried out normal daily‐life activities. Behavior and comments of patients during the experiment were used to determine the on and off periods by a trained observer. Behavior of the patients was measured using triaxial accelerometers, which were placed at six different positions on the body. Parameters related to hypokinesia (percentage movement), bradykinesia (mean velocity), and tremor (percentage peak frequencies above 4 Hz) were used to distinguish between on and off states. The on–off detection was evaluated using sensitivity and specificity. The performance for each patient was defined as the average of the sensitivity and specificity. The best performance to classify on and off states was obtained by analysis of movements in the frequency domain with a sensitivity of 0.97 and a specificity of 0.97. We conclude that our algorithm can distinguish between on and off states with a sensitivity and specificity near 0.97. This method, together with our previously published method to detect levodopa‐induced dyskinesia, can automatically assess the motor state of Parkinson's disease patients and can operate successfully in unsupervised ambulatory conditions. © 2005 Movement Disorder Society
Url:
DOI: 10.1002/mds.20633
Affiliations:
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Keijsers</name><affiliation wicri:level="3"><country xml:lang="fr">Pays-Bas</country><wicri:regionArea>Department of Biophysics, Institute for Neuroscience, Radboud University Nijmegen, Nijmegen</wicri:regionArea><placeName><settlement type="city">Nimègue</settlement><region type="province" nuts="2">Gueldre</region></placeName></affiliation></author><author><name sortKey="Horstink, Martin W I M" sort="Horstink, Martin W I M" uniqKey="Horstink M" first="Martin W. I. M." last="Horstink">Martin W. I. M. Horstink</name><affiliation wicri:level="3"><country xml:lang="fr">Pays-Bas</country><wicri:regionArea>Department of Neurology, Institute for Neuroscience, University Medical Center St. Radboud, Nijmegen</wicri:regionArea><placeName><settlement type="city">Nimègue</settlement><region type="province" nuts="2">Gueldre</region></placeName></affiliation></author><author><name sortKey="Gielen, Stan C A M" sort="Gielen, Stan C A M" uniqKey="Gielen S" first="Stan C. A. M." last="Gielen">Stan C. 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Disord.</title><idno type="ISSN">0885-3185</idno><idno type="eISSN">1531-8257</idno><imprint><publisher>Wiley Subscription Services, Inc., A Wiley Company</publisher><pubPlace>Hoboken</pubPlace><date type="published" when="2006-01">2006-01</date><biblScope unit="volume">21</biblScope><biblScope unit="issue">1</biblScope><biblScope unit="page" from="34">34</biblScope><biblScope unit="page" to="44">44</biblScope></imprint><idno type="ISSN">0885-3185</idno></series><idno type="istex">58E313712B6666BD9F7481BF28CDB9096C95870F</idno><idno type="DOI">10.1002/mds.20633</idno><idno type="ArticleID">MDS20633</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0885-3185</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Parkinson's disease</term><term>accelerometers</term><term>automatic assessment</term><term>daily life</term><term>motor fluctuations</term></keywords></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">We developed an algorithm that distinguishes between on and off states in patients with Parkinson's disease during daily life activities. Twenty‐three patients were monitored continuously in a home‐like situation for approximately 3 hours while they carried out normal daily‐life activities. Behavior and comments of patients during the experiment were used to determine the on and off periods by a trained observer. Behavior of the patients was measured using triaxial accelerometers, which were placed at six different positions on the body. Parameters related to hypokinesia (percentage movement), bradykinesia (mean velocity), and tremor (percentage peak frequencies above 4 Hz) were used to distinguish between on and off states. The on–off detection was evaluated using sensitivity and specificity. The performance for each patient was defined as the average of the sensitivity and specificity. The best performance to classify on and off states was obtained by analysis of movements in the frequency domain with a sensitivity of 0.97 and a specificity of 0.97. We conclude that our algorithm can distinguish between on and off states with a sensitivity and specificity near 0.97. This method, together with our previously published method to detect levodopa‐induced dyskinesia, can automatically assess the motor state of Parkinson's disease patients and can operate successfully in unsupervised ambulatory conditions. © 2005 Movement Disorder Society</div></front></TEI><affiliations><list><country><li>Pays-Bas</li></country><region><li>Gueldre</li></region><settlement><li>Nimègue</li></settlement></list><tree><country name="Pays-Bas"><region name="Gueldre"><name sortKey="Keijsers, Noel L W" sort="Keijsers, Noel L W" uniqKey="Keijsers N" first="Noël L. W." last="Keijsers">Noël L. W. Keijsers</name></region><name sortKey="Gielen, Stan C A M" sort="Gielen, Stan C A M" uniqKey="Gielen S" first="Stan C. A. M." last="Gielen">Stan C. A. M. Gielen</name><name sortKey="Horstink, Martin W I M" sort="Horstink, Martin W I M" uniqKey="Horstink M" first="Martin W. I. M." last="Horstink">Martin W. I. M. Horstink</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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