Time series prediction of lung cancer patients' breathing pattern based on nonlinear dynamics.

Abstract

This study focuses on predicting breathing pattern, which is crucial to deal with system latency in the treatments of moving lung tumors. Predicting respiratory motion in real-time is challenging, due to the inherent chaotic nature of breathing patterns, i.e. sensitive dependence on initial conditions. In this work, nonlinear prediction methods are used to… (More)
DOI: 10.1016/j.ejmp.2015.01.018

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