Highlights
- •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.
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 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.
Keywords
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Article info
Publication history
Published online: February 25, 2015
Accepted:
January 28,
2015
Received in revised form:
December 30,
2014
Received:
July 22,
2014
Identification
Copyright
© 2015 Associazione Italiana di Fisica Medica. Published by Elsevier Inc. All rights reserved.
