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

Content Provider	IEEE Xplore Digital Library
Author	Tatinati, S. Veluvolu, K.C. Sun-Mog Hong Nazarpour, K.
Copyright Year	2014
Description	Author affiliation: Sch. of Electr. & Electron. Eng., Newcastle Univ., Newcastle upon Tyne, UK (Nazarpour, K.) \|\| Sch. of Electron. Eng., Kyungpook Nat. Univ., Daegu, South Korea (Tatinati, S.; Veluvolu, K.C.; Sun-Mog Hong)
Abstract	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 $CyberKnife^{TM}.$ 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.
Sponsorship	IEEE Eng. Med. Biol. Soc.
Starting Page	4204
Ending Page	4207
File Size	702252
Page Count	4
File Format	PDF
ISBN	9781424479290
ISSN	1557170X
DOI	10.1109/EMBC.2014.6944551
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Accuracy Tumors Databases Support vector machines Real-time systems Kalman filters Mathematical model
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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