Towards Active Tracking of Beating Heart Motion in the Presence of Arrhythmia for Robotic Assisted Beating Heart Surgery
Identifieur interne : 003155 ( Ncbi/Curation ); précédent : 003154; suivant : 003156Towards Active Tracking of Beating Heart Motion in the Presence of Arrhythmia for Robotic Assisted Beating Heart Surgery
Auteurs : E. Erdem Tuna [États-Unis] ; Jamshid H. Karimov [États-Unis] ; Taoming Liu [États-Unis] ; Özkan Bebek [Turquie] ; Kiyotaka Fukamachi [États-Unis] ; M. Cenk Çavu O Lu [États-Unis]Source :
- PLoS ONE [ 1932-6203 ] ; 2014.
Abstract
In robotic assisted beating heart surgery, the control architecture for heart motion tracking has stringent requirements in terms of bandwidth of the motion that needs to be tracked. In order to achieve sufficient tracking accuracy, feed-forward control algorithms, which rely on estimations of upcoming heart motion, have been proposed in the literature. However, performance of these feed-forward motion control algorithms under heart rhythm variations is an important concern. In their past work, the authors have demonstrated the effectiveness of a receding horizon model predictive control-based algorithm, which used generalized adaptive predictors, under constant and slowly varying heart rate conditions. This paper extends these studies to the case when the heart motion statistics change abruptly and significantly, such as during arrhythmias. A feasibility study is carried out to assess the motion tracking capabilities of the adaptive algorithms in the occurrence of arrhythmia during beating heart surgery. Specifically, the tracking performance of the algorithms is evaluated on prerecorded motion data, which is collected
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DOI: 10.1371/journal.pone.0102877
PubMed: 25048462
PubMed Central: 4105597
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Erdem" last="Tuna">E. Erdem Tuna</name><affiliation wicri:level="2"><nlm:aff id="aff1"><addr-line>Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, Ohio, United States of America</addr-line></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, Ohio</wicri:regionArea><placeName><region type="state">Ohio</region></placeName></affiliation></author><author><name sortKey="Karimov, Jamshid H" sort="Karimov, Jamshid H" uniqKey="Karimov J" first="Jamshid H." last="Karimov">Jamshid H. Karimov</name><affiliation wicri:level="2"><nlm:aff id="aff2"><addr-line>Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America</addr-line></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio</wicri:regionArea><placeName><region type="state">Ohio</region></placeName></affiliation></author><author><name sortKey="Liu, Taoming" sort="Liu, Taoming" uniqKey="Liu T" first="Taoming" last="Liu">Taoming Liu</name><affiliation wicri:level="2"><nlm:aff id="aff1"><addr-line>Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, Ohio, United States of America</addr-line></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, Ohio</wicri:regionArea><placeName><region type="state">Ohio</region></placeName></affiliation></author><author><name sortKey="Bebek, Ozkan" sort="Bebek, Ozkan" uniqKey="Bebek O" first="Özkan" last="Bebek">Özkan Bebek</name><affiliation wicri:level="1"><nlm:aff id="aff3"><addr-line>Department of Mechanical Engineering, Özyeğin University, Istanbul, Turkey</addr-line></nlm:aff><country xml:lang="fr">Turquie</country><wicri:regionArea>Department of Mechanical Engineering, Özyeğin University, Istanbul</wicri:regionArea><wicri:noRegion>Istanbul</wicri:noRegion></affiliation></author><author><name sortKey="Fukamachi, Kiyotaka" sort="Fukamachi, Kiyotaka" uniqKey="Fukamachi K" first="Kiyotaka" last="Fukamachi">Kiyotaka Fukamachi</name><affiliation wicri:level="2"><nlm:aff id="aff2"><addr-line>Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America</addr-line></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio</wicri:regionArea><placeName><region type="state">Ohio</region></placeName></affiliation></author><author><name sortKey="Cavu O Lu, M Cenk" sort="Cavu O Lu, M Cenk" uniqKey="Cavu O Lu M" first="M. Cenk" last="Çavu O Lu">M. Cenk Çavu O Lu</name><affiliation wicri:level="2"><nlm:aff id="aff1"><addr-line>Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, Ohio, United States of America</addr-line></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, Ohio</wicri:regionArea><placeName><region type="state">Ohio</region></placeName></affiliation></author></analytic><series><title level="j">PLoS ONE</title><idno type="eISSN">1932-6203</idno><imprint><date when="2014">2014</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>In robotic assisted beating heart surgery, the control architecture for heart motion tracking has stringent requirements in terms of bandwidth of the motion that needs to be tracked. In order to achieve sufficient tracking accuracy, feed-forward control algorithms, which rely on estimations of upcoming heart motion, have been proposed in the literature. However, performance of these feed-forward motion control algorithms under heart rhythm variations is an important concern. In their past work, the authors have demonstrated the effectiveness of a receding horizon model predictive control-based algorithm, which used generalized adaptive predictors, under constant and slowly varying heart rate conditions. This paper extends these studies to the case when the heart motion statistics change abruptly and significantly, such as during arrhythmias. A feasibility study is carried out to assess the motion tracking capabilities of the adaptive algorithms in the occurrence of arrhythmia during beating heart surgery. Specifically, the tracking performance of the algorithms is evaluated on prerecorded motion data, which is collected <italic>in vivo</italic> and includes heart rhythm irregularities. The algorithms are tested using both simulations and bench experiments on a three degree-of-freedom robotic test bed. They are also compared with a position-plus-derivative controller as well as a receding horizon model predictive controller that employs an extended Kalman filter algorithm for predicting future heart motion.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Ginhoux, R" uniqKey="Ginhoux R">R Ginhoux</name></author><author><name sortKey="Gangloff, Ja" uniqKey="Gangloff J">JA Gangloff</name></author><author><name sortKey="Demathelin, Mf" uniqKey="Demathelin M">MF DeMathelin</name></author><author><name sortKey="Soler, L" uniqKey="Soler L">L Soler</name></author><author><name sortKey="Leroy, J" uniqKey="Leroy J">J Leroy</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Richa, R" uniqKey="Richa R">R Richa</name></author><author><name sortKey="B, Ap" uniqKey="B A">AP Bó</name></author><author><name sortKey="Poignet, P" uniqKey="Poignet P">P 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