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Real-time prediction of respiratory motion traces for radiotherapy with ensemble learning

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.

Publication metadata

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


DOI: 10.1109/EMBC.2014.6944551

Library holdings: Search Newcastle University Library for this item

ISBN: 9781424479290