Automatic subthalamic nucleus detection from microelectrode recordings based on noise level and neuronal activity
Identifieur interne : 000087 ( Main/Exploration ); précédent : 000086; suivant : 000088Automatic subthalamic nucleus detection from microelectrode recordings based on noise level and neuronal activity
Auteurs : Hayriye Cagnan [Royaume-Uni] ; Kevin Dolan [Pays-Bas] ; Xuan He [Pays-Bas] ; Maria Fiorella Contarino [Pays-Bas] ; Richard Schuurman [Pays-Bas] ; Pepijn Van Den Munckhof [Pays-Bas] ; Wytse J. Wadman [Pays-Bas] ; Lo Bour [Pays-Bas] ; Hubert C F. Martens [Pays-Bas]Source :
- Journal of Neural Engineering [ 1741-2560 ] ; 2011.
Abstract
Microelectrode recording (MER) along surgical trajectories is commonly applied for refinement of the target location during deep brain stimulation (DBS) surgery. In this study, we utilize automatically detected MER features in order to locate the subthalamic nucleus (STN) employing an unsupervised algorithm. The automated algorithm makes use of background noise level, compound firing rate and power spectral density along the trajectory and applies a threshold-based method to detect the dorsal and the ventral borders of the STN. Depending on the combination of measures used for detection of the borders, the algorithm allocates confidence levels for the annotation made (i.e. high, medium and low). The algorithm has been applied to 258 trajectories obtained from 84 STN DBS implantations. MERs used in this study have not been pre-selected or pre-processed and include all the viable measurements made. Out of 258 trajectories, 239 trajectories were annotated by the surgical team as containing the STN versus 238 trajectories by the automated algorithm. The agreement level between the automatic annotations and the surgical annotations is 88. Taking the surgical annotations as the golden standard, across all trajectories, the algorithm made true positive annotations in 231 trajectories, true negative annotations in 12 trajectories, false positive annotations in 7 trajectories and false negative annotations in 8 trajectories. We conclude that our algorithm is accurate and reliable in automatically identifying the STN and locating the dorsal and ventral borders of the nucleus, and in a near future could be implemented for on-line intra-operative use.
Url:
Affiliations:
Links toward previous steps (curation, corpus...)
Le document en format XML
<record><TEI wicri:istexFullTextTei="biblStruct"><teiHeader><fileDesc><titleStmt><title>Automatic subthalamic nucleus detection from microelectrode recordings based on noise level and neuronal activity</title><author><name sortKey="Cagnan, Hayriye" sort="Cagnan, Hayriye" uniqKey="Cagnan H" first="Hayriye" last="Cagnan">Hayriye Cagnan</name></author><author><name sortKey="Dolan, Kevin" sort="Dolan, Kevin" uniqKey="Dolan K" first="Kevin" last="Dolan">Kevin Dolan</name></author><author><name sortKey="He, Xuan" sort="He, Xuan" uniqKey="He X" first="Xuan" last="He">Xuan He</name></author><author><name sortKey="Contarino, Maria Fiorella" sort="Contarino, Maria Fiorella" uniqKey="Contarino M" first="Maria Fiorella" last="Contarino">Maria Fiorella Contarino</name></author><author><name sortKey="Schuurman, Richard" sort="Schuurman, Richard" uniqKey="Schuurman R" first="Richard" last="Schuurman">Richard Schuurman</name></author><author><name sortKey="Van Den Munckhof, Pepijn" sort="Van Den Munckhof, Pepijn" uniqKey="Van Den Munckhof P" first="Pepijn" last="Van Den Munckhof">Pepijn Van Den Munckhof</name></author><author><name sortKey="Wadman, Wytse J" sort="Wadman, Wytse J" uniqKey="Wadman W" first="Wytse J" last="Wadman">Wytse J. Wadman</name></author><author><name sortKey="Bour, Lo" sort="Bour, Lo" uniqKey="Bour L" first="Lo" last="Bour">Lo Bour</name></author><author><name sortKey="Martens, Hubert C F" sort="Martens, Hubert C F" uniqKey="Martens H" first="Hubert C F" last="Martens">Hubert C F. Martens</name></author></titleStmt><publicationStmt><idno type="wicri:source">ISTEX</idno><idno type="RBID">ISTEX:AE48434E0788CA8C26BA50FD02CDF77DA015C6C1</idno><date when="2011" year="2011">2011</date><idno type="url">https://api.istex.fr/document/AE48434E0788CA8C26BA50FD02CDF77DA015C6C1/fulltext/pdf</idno><idno type="wicri:Area/Main/Corpus">000648</idno><idno type="wicri:Area/Main/Curation">000565</idno><idno type="wicri:Area/Main/Exploration">000087</idno></publicationStmt><sourceDesc><biblStruct><analytic><title level="a">Automatic subthalamic nucleus detection from microelectrode recordings based on noise level and neuronal activity</title><author><name sortKey="Cagnan, Hayriye" sort="Cagnan, Hayriye" uniqKey="Cagnan H" first="Hayriye" last="Cagnan">Hayriye Cagnan</name><affiliation wicri:level="4"><country xml:lang="fr">Royaume-Uni</country><wicri:regionArea>Department of Clinical Neurology, University of Oxford</wicri:regionArea><placeName><settlement type="city">Oxford</settlement><region type="nation">Angleterre</region><region type="région" nuts="1">Oxfordshire</region></placeName><orgName type="university">Université d'Oxford</orgName></affiliation><affiliation><wicri:noCountry code="no comma">Author to whom any correspondence should be addressed</wicri:noCountry></affiliation><affiliation wicri:level="1"><country wicri:rule="url">Royaume-Uni</country></affiliation></author><author><name sortKey="Dolan, Kevin" sort="Dolan, Kevin" uniqKey="Dolan K" first="Kevin" last="Dolan">Kevin Dolan</name><affiliation wicri:level="1"><country xml:lang="fr">Pays-Bas</country><wicri:regionArea>Philips Research Laboratories, Eindhoven, 5656 AE</wicri:regionArea><wicri:noRegion>5656 AE</wicri:noRegion></affiliation></author><author><name sortKey="He, Xuan" sort="He, Xuan" uniqKey="He X" first="Xuan" last="He">Xuan He</name><affiliation wicri:level="1"><country xml:lang="fr">Pays-Bas</country><wicri:regionArea>Philips Research Laboratories, Eindhoven, 5656 AE</wicri:regionArea><wicri:noRegion>5656 AE</wicri:noRegion></affiliation></author><author><name sortKey="Contarino, Maria Fiorella" sort="Contarino, Maria Fiorella" uniqKey="Contarino M" first="Maria Fiorella" last="Contarino">Maria Fiorella Contarino</name><affiliation wicri:level="1"><country xml:lang="fr">Pays-Bas</country><wicri:regionArea>Academic Medical Center, Universiteit van Amsterdam</wicri:regionArea><wicri:noRegion>Universiteit van Amsterdam</wicri:noRegion></affiliation></author><author><name sortKey="Schuurman, Richard" sort="Schuurman, Richard" uniqKey="Schuurman R" first="Richard" last="Schuurman">Richard Schuurman</name><affiliation wicri:level="1"><country xml:lang="fr">Pays-Bas</country><wicri:regionArea>Academic Medical Center, Universiteit van Amsterdam</wicri:regionArea><wicri:noRegion>Universiteit van Amsterdam</wicri:noRegion></affiliation></author><author><name sortKey="Van Den Munckhof, Pepijn" sort="Van Den Munckhof, Pepijn" uniqKey="Van Den Munckhof P" first="Pepijn" last="Van Den Munckhof">Pepijn Van Den Munckhof</name><affiliation wicri:level="1"><country xml:lang="fr">Pays-Bas</country><wicri:regionArea>Academic Medical Center, Universiteit van Amsterdam</wicri:regionArea><wicri:noRegion>Universiteit van Amsterdam</wicri:noRegion></affiliation></author><author><name sortKey="Wadman, Wytse J" sort="Wadman, Wytse J" uniqKey="Wadman W" first="Wytse J" last="Wadman">Wytse J. Wadman</name><affiliation wicri:level="1"><country xml:lang="fr">Pays-Bas</country><wicri:regionArea>SILS, Universiteit van Amsterdam</wicri:regionArea><wicri:noRegion>Universiteit van Amsterdam</wicri:noRegion></affiliation></author><author><name sortKey="Bour, Lo" sort="Bour, Lo" uniqKey="Bour L" first="Lo" last="Bour">Lo Bour</name><affiliation wicri:level="1"><country xml:lang="fr">Pays-Bas</country><wicri:regionArea>Academic Medical Center, Universiteit van Amsterdam</wicri:regionArea><wicri:noRegion>Universiteit van Amsterdam</wicri:noRegion></affiliation></author><author><name sortKey="Martens, Hubert C F" sort="Martens, Hubert C F" uniqKey="Martens H" first="Hubert C F" last="Martens">Hubert C F. Martens</name><affiliation wicri:level="1"><country xml:lang="fr">Pays-Bas</country><wicri:regionArea>Philips Research Laboratories, Eindhoven, 5656 AE</wicri:regionArea><wicri:noRegion>5656 AE</wicri:noRegion></affiliation></author></analytic><monogr></monogr><series><title level="j">Journal of Neural Engineering</title><title level="j" type="abbrev">J. Neural Eng.</title><idno type="ISSN">1741-2560</idno><imprint><publisher>IOP Publishing</publisher><date type="published" when="2011">2011</date><biblScope unit="volume">8</biblScope><biblScope unit="issue">4</biblScope><biblScope unit="page" from="1">1</biblScope><biblScope unit="page" to="9">9</biblScope><biblScope unit="production">Printed in the UK</biblScope></imprint></series><idno type="istex">AE48434E0788CA8C26BA50FD02CDF77DA015C6C1</idno><idno type="DOI">10.1088/1741-2560/8/4/046006</idno><idno type="PII">S1741-2560(11)85116-5</idno><idno type="articleID">385116</idno><idno type="articleNumber">046006</idno></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract">Microelectrode recording (MER) along surgical trajectories is commonly applied for refinement of the target location during deep brain stimulation (DBS) surgery. In this study, we utilize automatically detected MER features in order to locate the subthalamic nucleus (STN) employing an unsupervised algorithm. The automated algorithm makes use of background noise level, compound firing rate and power spectral density along the trajectory and applies a threshold-based method to detect the dorsal and the ventral borders of the STN. Depending on the combination of measures used for detection of the borders, the algorithm allocates confidence levels for the annotation made (i.e. high, medium and low). The algorithm has been applied to 258 trajectories obtained from 84 STN DBS implantations. MERs used in this study have not been pre-selected or pre-processed and include all the viable measurements made. Out of 258 trajectories, 239 trajectories were annotated by the surgical team as containing the STN versus 238 trajectories by the automated algorithm. The agreement level between the automatic annotations and the surgical annotations is 88. Taking the surgical annotations as the golden standard, across all trajectories, the algorithm made true positive annotations in 231 trajectories, true negative annotations in 12 trajectories, false positive annotations in 7 trajectories and false negative annotations in 8 trajectories. We conclude that our algorithm is accurate and reliable in automatically identifying the STN and locating the dorsal and ventral borders of the nucleus, and in a near future could be implemented for on-line intra-operative use.</div></front></TEI><affiliations><list><country><li>Pays-Bas</li><li>Royaume-Uni</li></country><region><li>Angleterre</li><li>Oxfordshire</li></region><settlement><li>Oxford</li></settlement><orgName><li>Université d'Oxford</li></orgName></list><tree><country name="Royaume-Uni"><region name="Angleterre"><name sortKey="Cagnan, Hayriye" sort="Cagnan, Hayriye" uniqKey="Cagnan H" first="Hayriye" last="Cagnan">Hayriye Cagnan</name></region><name sortKey="Cagnan, Hayriye" sort="Cagnan, Hayriye" uniqKey="Cagnan H" first="Hayriye" last="Cagnan">Hayriye Cagnan</name></country><country name="Pays-Bas"><noRegion><name sortKey="Dolan, Kevin" sort="Dolan, Kevin" uniqKey="Dolan K" first="Kevin" last="Dolan">Kevin Dolan</name></noRegion><name sortKey="Bour, Lo" sort="Bour, Lo" uniqKey="Bour L" first="Lo" last="Bour">Lo Bour</name><name sortKey="Contarino, Maria Fiorella" sort="Contarino, Maria Fiorella" uniqKey="Contarino M" first="Maria Fiorella" last="Contarino">Maria Fiorella Contarino</name><name sortKey="He, Xuan" sort="He, Xuan" uniqKey="He X" first="Xuan" last="He">Xuan He</name><name sortKey="Martens, Hubert C F" sort="Martens, Hubert C F" uniqKey="Martens H" first="Hubert C F" last="Martens">Hubert C F. Martens</name><name sortKey="Schuurman, Richard" sort="Schuurman, Richard" uniqKey="Schuurman R" first="Richard" last="Schuurman">Richard Schuurman</name><name sortKey="Van Den Munckhof, Pepijn" sort="Van Den Munckhof, Pepijn" uniqKey="Van Den Munckhof P" first="Pepijn" last="Van Den Munckhof">Pepijn Van Den Munckhof</name><name sortKey="Wadman, Wytse J" sort="Wadman, Wytse J" uniqKey="Wadman W" first="Wytse J" last="Wadman">Wytse J. Wadman</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Wicri/Sante/explor/ParkinsonV1/Data/Main/Exploration
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 000087 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/Main/Exploration/biblio.hfd -nk 000087 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien
|wiki= Wicri/Sante
|area= ParkinsonV1
|flux= Main
|étape= Exploration
|type= RBID
|clé= ISTEX:AE48434E0788CA8C26BA50FD02CDF77DA015C6C1
|texte= Automatic subthalamic nucleus detection from microelectrode recordings based on noise level and neuronal activity
}}
| This area was generated with Dilib version V0.6.23. |  |