This paper presents a medical application of the intelligent sensing and monitoring, a new
lung tumor motion prediction method for tumor following radiation therapy. An essential
core of the method is accurate estimation of complex fluctuation of time-varying periodical
nature of lung tumor motion. Such estimation is achieved by using a novel multiple
time-varying seasonal autoregressive (TVSAR) model in which several windows of different
time-lengths are used to calculate correlation based fluctuation of periodic nature in
the motion. The proposed method provides the prediction as a combination of those based
on different window lengths. Multiple regression (MR), multilayer perceptron (MLP) and
support vector regression (SVR) are used to combine and the prediction performances are
evaluated by using clinical lung tumor motion. The proposed methods with the combined
predictions showed high accurate prediction and are superior to the single different predictions.
The average errors of MR, MLP, and SVR were 0.8455,0.8507, and 0.7530 mm
at 0.5 s ahead, respectively. The results are clinically sufficient and thus clearly demonstrate
that the proposed TVSAR with an appropriate combination method is useful for
improving the prediction performance.
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