Automatic detection of movement disorders using recordings of rapid alternating movements.
Identifieur interne : 002995 ( Main/Exploration ); précédent : 002994; suivant : 002996Automatic detection of movement disorders using recordings of rapid alternating movements.
Auteurs : Eric J. Fimbel [Canada] ; Pedro Perez Domingo ; Daniel Lamoureux ; Anne BeuterSource :
- Journal of neuroscience methods [ 0165-0270 ] ; 2005.
English descriptors
- KwdEn :
- Adult, Biomechanical Phenomena (instrumentation), Biomechanical Phenomena (methods), Electrodiagnosis (instrumentation), Electrodiagnosis (methods), Female, Forearm (physiology), Humans, Male, Middle Aged, Movement (physiology), Parkinson Disease (diagnosis), Parkinson Disease (physiopathology), Signal Processing, Computer-Assisted (instrumentation).
- MESH :
- diagnosis : Parkinson Disease.
- instrumentation : Biomechanical Phenomena, Electrodiagnosis, Signal Processing, Computer-Assisted.
- methods : Biomechanical Phenomena, Electrodiagnosis.
- physiology : Forearm, Movement.
- physiopathology : Parkinson Disease.
- Adult, Female, Humans, Male, Middle Aged.
Abstract
The present work assesses the potential of rapid alternating movement analysis for detecting movement disorders like Parkinson's disease. Rapid alternating wrist movements were recorded by a diadochokinesimeter for patients with Parkinson's disease (n=10) and healthy controls (n=20). An index of irregularity was computed for each individual as the density of jerk singularities (i.e. zero-crossings) during the movements. Several scales of analysis (i.e. "coarseness") were used for detecting the jerk events and two methods were compared for all of these scales: (1) automatic classification by means of a threshold that optimally separates the indexes of irregularity of the patients from those of the controls, and (2) statistical decision (normal or abnormal) based upon a distribution of indexes of irregularity obtained from a large population of normal subjects. The results showed that (1) two scales of analysis were sufficient and that (2) both methods presented similar performances (e.g. sensitivity=1.00, specificity=0.85, efficiency=0.90). However, statistical decision should be preferred because of its simplicity. The possibility of automatic detection of movement disorders from alternating movements is discussed.
DOI: 10.1016/j.jneumeth.2005.02.007
PubMed: 16054508
Affiliations:
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Le document en format XML
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Fimbel</name><affiliation wicri:level="1"><nlm:affiliation>Département de génie électrique, Ecole de technologie supérieure, 1100 Notre Dame Ouest, Montréal, Québec, Canada H3C 1K3. efimbel@ele.etsmtl.ca</nlm:affiliation><country>Canada</country><wicri:regionArea>Département de génie électrique, Ecole de technologie supérieure, 1100 Notre Dame Ouest, Montréal, Québec</wicri:regionArea><wicri:noRegion>Québec</wicri:noRegion></affiliation></author><author><name sortKey="Domingo, Pedro Perez" sort="Domingo, Pedro Perez" uniqKey="Domingo P" first="Pedro Perez" last="Domingo">Pedro Perez Domingo</name></author><author><name sortKey="Lamoureux, Daniel" sort="Lamoureux, Daniel" uniqKey="Lamoureux D" first="Daniel" last="Lamoureux">Daniel Lamoureux</name></author><author><name sortKey="Beuter, Anne" sort="Beuter, Anne" uniqKey="Beuter A" first="Anne" last="Beuter">Anne Beuter</name></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2005">2005</date><idno type="RBID">pubmed:16054508</idno><idno type="pmid">16054508</idno><idno type="doi">10.1016/j.jneumeth.2005.02.007</idno><idno type="wicri:Area/PubMed/Corpus">001241</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">001241</idno><idno type="wicri:Area/PubMed/Curation">001241</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Curation">001241</idno><idno type="wicri:Area/PubMed/Checkpoint">001241</idno><idno type="wicri:explorRef" wicri:stream="Checkpoint" wicri:step="PubMed">001241</idno><idno type="wicri:Area/Ncbi/Merge">000557</idno><idno type="wicri:Area/Ncbi/Curation">000557</idno><idno type="wicri:Area/Ncbi/Checkpoint">000557</idno><idno type="wicri:doubleKey">0165-0270:2005:Fimbel E:automatic:detection:of</idno><idno type="wicri:Area/Main/Merge">002D24</idno><idno type="wicri:Area/Main/Curation">002995</idno><idno type="wicri:Area/Main/Exploration">002995</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Automatic detection of movement disorders using recordings of rapid alternating movements.</title><author><name sortKey="Fimbel, Eric J" sort="Fimbel, Eric J" uniqKey="Fimbel E" first="Eric J" last="Fimbel">Eric J. Fimbel</name><affiliation wicri:level="1"><nlm:affiliation>Département de génie électrique, Ecole de technologie supérieure, 1100 Notre Dame Ouest, Montréal, Québec, Canada H3C 1K3. efimbel@ele.etsmtl.ca</nlm:affiliation><country>Canada</country><wicri:regionArea>Département de génie électrique, Ecole de technologie supérieure, 1100 Notre Dame Ouest, Montréal, Québec</wicri:regionArea><wicri:noRegion>Québec</wicri:noRegion></affiliation></author><author><name sortKey="Domingo, Pedro Perez" sort="Domingo, Pedro Perez" uniqKey="Domingo P" first="Pedro Perez" last="Domingo">Pedro Perez Domingo</name></author><author><name sortKey="Lamoureux, Daniel" sort="Lamoureux, Daniel" uniqKey="Lamoureux D" first="Daniel" last="Lamoureux">Daniel Lamoureux</name></author><author><name sortKey="Beuter, Anne" sort="Beuter, Anne" uniqKey="Beuter A" first="Anne" last="Beuter">Anne Beuter</name></author></analytic><series><title level="j">Journal of neuroscience methods</title><idno type="ISSN">0165-0270</idno><imprint><date when="2005" type="published">2005</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Adult</term><term>Biomechanical Phenomena (instrumentation)</term><term>Biomechanical Phenomena (methods)</term><term>Electrodiagnosis (instrumentation)</term><term>Electrodiagnosis (methods)</term><term>Female</term><term>Forearm (physiology)</term><term>Humans</term><term>Male</term><term>Middle Aged</term><term>Movement (physiology)</term><term>Parkinson Disease (diagnosis)</term><term>Parkinson Disease (physiopathology)</term><term>Signal Processing, Computer-Assisted (instrumentation)</term></keywords><keywords scheme="MESH" qualifier="diagnosis" xml:lang="en"><term>Parkinson Disease</term></keywords><keywords scheme="MESH" qualifier="instrumentation" xml:lang="en"><term>Biomechanical Phenomena</term><term>Electrodiagnosis</term><term>Signal Processing, Computer-Assisted</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Biomechanical Phenomena</term><term>Electrodiagnosis</term></keywords><keywords scheme="MESH" qualifier="physiology" xml:lang="en"><term>Forearm</term><term>Movement</term></keywords><keywords scheme="MESH" qualifier="physiopathology" xml:lang="en"><term>Parkinson Disease</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Adult</term><term>Female</term><term>Humans</term><term>Male</term><term>Middle Aged</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">The present work assesses the potential of rapid alternating movement analysis for detecting movement disorders like Parkinson's disease. Rapid alternating wrist movements were recorded by a diadochokinesimeter for patients with Parkinson's disease (n=10) and healthy controls (n=20). An index of irregularity was computed for each individual as the density of jerk singularities (i.e. zero-crossings) during the movements. Several scales of analysis (i.e. "coarseness") were used for detecting the jerk events and two methods were compared for all of these scales: (1) automatic classification by means of a threshold that optimally separates the indexes of irregularity of the patients from those of the controls, and (2) statistical decision (normal or abnormal) based upon a distribution of indexes of irregularity obtained from a large population of normal subjects. The results showed that (1) two scales of analysis were sufficient and that (2) both methods presented similar performances (e.g. sensitivity=1.00, specificity=0.85, efficiency=0.90). However, statistical decision should be preferred because of its simplicity. The possibility of automatic detection of movement disorders from alternating movements is discussed.</div></front></TEI><affiliations><list><country><li>Canada</li></country></list><tree><noCountry><name sortKey="Beuter, Anne" sort="Beuter, Anne" uniqKey="Beuter A" first="Anne" last="Beuter">Anne Beuter</name><name sortKey="Domingo, Pedro Perez" sort="Domingo, Pedro Perez" uniqKey="Domingo P" first="Pedro Perez" last="Domingo">Pedro Perez Domingo</name><name sortKey="Lamoureux, Daniel" sort="Lamoureux, Daniel" uniqKey="Lamoureux D" first="Daniel" last="Lamoureux">Daniel Lamoureux</name></noCountry><country name="Canada"><noRegion><name sortKey="Fimbel, Eric J" sort="Fimbel, Eric J" uniqKey="Fimbel E" first="Eric J" last="Fimbel">Eric J. Fimbel</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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