- •Breathing is a chaotic multidimensional nonlinear dynamical system.
- •State-space methodologies operate with the time-series in its true dimension.
- •LAM and regularized nonlinear prediction perform better than linear prediction.
- •LAM models perform as well as more complicated local linear models.
- •LAM models are computationally less expensive and hence more desirable.
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 predict the short-term evolution of the respiratory system for 62 patients, whose breathing time series was acquired using respiratory position management (RPM) system. Single step and N-point multi step prediction are performed for sampling rates of 5 Hz and 10 Hz. We compare the employed non-linear prediction methods with respect to prediction accuracy to Adaptive Infinite Impulse Response (IIR) prediction filters. A Local Average Model (LAM) and local linear models (LLMs) combined with a set of linear regularization techniques to solve ill-posed regression problems are implemented. For all sampling frequencies both single step and N-point multi step prediction results obtained using LAM and LLM with regularization methods perform better than IIR prediction filters for the selected sample patients. Moreover, since the simple LAM model performs as well as the more complicated LLM models in our patient sample, its use for non-linear prediction is recommended.
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Published online: February 25, 2015
Accepted: January 28, 2015
Received in revised form: December 30, 2014
Received: July 22, 2014
© 2015 Associazione Italiana di Fisica Medica. Published by Elsevier Inc. All rights reserved.