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

R. P. Tolakanahalli, D. K. Tewatia, W. A. Tomé

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


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 5Hz and 10Hz. 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.

Original languageEnglish (US)
Pages (from-to)257-265
Number of pages9
JournalPhysica Medica
Issue number3
StatePublished - May 1 2015


  • Breathing pattern
  • Embedding dimension
  • Infinite response prediction filters
  • Lung cancer
  • Non-linear dynamics
  • Non-linear prediction
  • Prediction
  • Time delay

ASJC Scopus subject areas

  • Biophysics
  • Radiology Nuclear Medicine and imaging
  • Physics and Astronomy(all)


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