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Lookup NU author(s): Dr Kalyana Veluvolu, Professor Kianoush Nazarpour
© 2014 IEEE. In this paper, we introduce a hybrid method for prediction of respiratory motion to overcome the inherent delay in robotic radiosurgery while treating lung tumors. The hybrid method adopts least squares support vector machine (LS-SVM) based ensemble learning approach to exploit the relative advantages of the individual methods local circular motion (LCM) with extended Kalman filter (EKF) and autoregressive moving average (ARMA) model with fading memory Kalman filter (FMKF). The efficiency the proposed hybrid approach was assessed with the real respiratory motion traces of 31 patients while treating with CyberKnife<sup>TM</sup>. Results show that the proposed hybrid method improves the prediction accuracy by approximately 10% for prediction horizons of 460 ms compared to the existing methods.
Author(s): Tatinati S, Veluvolu KC, Hong S-M, Nazarpour K
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2014)
Year of Conference: 2014
Pages: 4204-4207
Online publication date: 06/11/2014
Acceptance date: 26/08/2014
Date deposited: 29/01/2018
ISSN: 1558-4615
Publisher: IEEE
URL: https://doi.org/10.1109/EMBC.2014.6944551
DOI: 10.1109/EMBC.2014.6944551
Library holdings: Search Newcastle University Library for this item
ISBN: 9781424479290