Automatic subthalamic nucleus detection from microelectrode recordings based on noise level and neuronal activity.
Identifieur interne : 002482 ( Main/Exploration ); précédent : 002481; suivant : 002483Automatic subthalamic nucleus detection from microelectrode recordings based on noise level and neuronal activity.
Auteurs : Hayriye Cagnan [Royaume-Uni] ; Kevin Dolan ; Xuan He ; Maria Fiorella Contarino ; Richard Schuurman ; Pepijn Van Den Munckhof ; Wytse J. Wadman ; Lo Bour ; Hubert C F. MartensSource :
- Journal of neural engineering [ 1741-2552 ] ; 2011.
Descripteurs français
- KwdFr :
- Algorithmes, Artéfacts, Bruit (effets indésirables), Humains, Microélectrodes, Neurones (physiologie), Noyau subthalamique (), Noyau subthalamique (anatomie et histologie), Noyau subthalamique (physiologie), Reproductibilité des résultats, Stimulation cérébrale profonde (), Substantia nigra (anatomie et histologie), Substantia nigra (physiologie), Traitement du signal assisté par ordinateur, Électrodes implantées.
- MESH :
- anatomie et histologie : Noyau subthalamique, Substantia nigra.
- effets indésirables : Bruit.
- physiologie : Neurones, Noyau subthalamique, Substantia nigra.
- Algorithmes, Artéfacts, Humains, Microélectrodes, Noyau subthalamique, Reproductibilité des résultats, Stimulation cérébrale profonde, Traitement du signal assisté par ordinateur, Électrodes implantées.
English descriptors
- KwdEn :
- Algorithms, Artifacts, Deep Brain Stimulation (methods), Electrodes, Implanted, Humans, Microelectrodes, Neurons (physiology), Noise (adverse effects), Reproducibility of Results, Signal Processing, Computer-Assisted, Substantia Nigra (anatomy & histology), Substantia Nigra (physiology), Subthalamic Nucleus (anatomy & histology), Subthalamic Nucleus (physiology), Subthalamic Nucleus (surgery).
- MESH :
- adverse effects : Noise.
- anatomy & histology : Substantia Nigra, Subthalamic Nucleus.
- methods : Deep Brain Stimulation.
- physiology : Neurons, Substantia Nigra, Subthalamic Nucleus.
- surgery : Subthalamic Nucleus.
- Algorithms, Artifacts, Electrodes, Implanted, Humans, Microelectrodes, Reproducibility of Results, Signal Processing, Computer-Assisted.
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.
DOI: 10.1088/1741-2560/8/4/046006
PubMed: 21628771
Affiliations:
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Martens</name></author></analytic><series><title level="j">Journal of neural engineering</title><idno type="eISSN">1741-2552</idno><imprint><date when="2011" type="published">2011</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term><term>Artifacts</term><term>Deep Brain Stimulation (methods)</term><term>Electrodes, Implanted</term><term>Humans</term><term>Microelectrodes</term><term>Neurons (physiology)</term><term>Noise (adverse effects)</term><term>Reproducibility of Results</term><term>Signal Processing, Computer-Assisted</term><term>Substantia Nigra (anatomy & histology)</term><term>Substantia Nigra (physiology)</term><term>Subthalamic Nucleus (anatomy & histology)</term><term>Subthalamic Nucleus (physiology)</term><term>Subthalamic Nucleus (surgery)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Algorithmes</term><term>Artéfacts</term><term>Bruit (effets indésirables)</term><term>Humains</term><term>Microélectrodes</term><term>Neurones (physiologie)</term><term>Noyau subthalamique ()</term><term>Noyau subthalamique (anatomie et histologie)</term><term>Noyau subthalamique (physiologie)</term><term>Reproductibilité des résultats</term><term>Stimulation cérébrale profonde ()</term><term>Substantia nigra (anatomie et histologie)</term><term>Substantia nigra (physiologie)</term><term>Traitement du signal assisté par ordinateur</term><term>Électrodes implantées</term></keywords><keywords scheme="MESH" qualifier="adverse effects" xml:lang="en"><term>Noise</term></keywords><keywords scheme="MESH" qualifier="anatomie et histologie" xml:lang="fr"><term>Noyau subthalamique</term><term>Substantia nigra</term></keywords><keywords scheme="MESH" qualifier="anatomy & histology" xml:lang="en"><term>Substantia Nigra</term><term>Subthalamic Nucleus</term></keywords><keywords scheme="MESH" qualifier="effets indésirables" xml:lang="fr"><term>Bruit</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Deep Brain Stimulation</term></keywords><keywords scheme="MESH" qualifier="physiologie" xml:lang="fr"><term>Neurones</term><term>Noyau subthalamique</term><term>Substantia nigra</term></keywords><keywords scheme="MESH" qualifier="physiology" xml:lang="en"><term>Neurons</term><term>Substantia Nigra</term><term>Subthalamic Nucleus</term></keywords><keywords scheme="MESH" qualifier="surgery" xml:lang="en"><term>Subthalamic Nucleus</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Artifacts</term><term>Electrodes, Implanted</term><term>Humans</term><term>Microelectrodes</term><term>Reproducibility of Results</term><term>Signal Processing, Computer-Assisted</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Algorithmes</term><term>Artéfacts</term><term>Humains</term><term>Microélectrodes</term><term>Noyau subthalamique</term><term>Reproductibilité des résultats</term><term>Stimulation cérébrale profonde</term><term>Traitement du signal assisté par ordinateur</term><term>Électrodes implantées</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">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>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><noCountry><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="Dolan, Kevin" sort="Dolan, Kevin" uniqKey="Dolan K" first="Kevin" last="Dolan">Kevin Dolan</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></noCountry><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></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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