Intelligent sensing of biomedical signals - Lung tumor motion prediction for accurate radiotherapy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

This paper presents a medical application of the intelligent sensing, 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-variant periodical nature of lung tumor motion. Such estimation can be achieved by using a novel multiple time-variant seasonal autoregressive integral moving average (TVSARIMA) model in which several windows of different lengths are used to calculate correlation based time-variant periods of the motion. The proposed method provides the resulting prediction as a combination of those based on different window lengths. We have compared unweighted average, multiple regression, and multilayer perceptron (MLP) for the combinations with some conventional predictions by using real data of lung tumor motion. The proposed methods with the multiple regression and MLP based combinations showed high accurate prediction and are superior to the single TVSARIMA based prediction. The best prediction performance was achieved by using the MLP based combination. The average errors were 0.7953±0.0243 mm at 0.5 s ahead and 0.8581±0.0510 mm at 1.0 s ahead predictions, respectively. The results of the proposed method are clinically sufficient and superior to the conventional methods. Thus the proposed TVSARIMA with an appropriate combination method is useful for improving the prediction performance.

Original languageEnglish
Title of host publicationIEEE SSCI 2011 - Symposium Series on Computational Intelligence - CompSens 2011
Subtitle of host publication2011 IEEE Workshop on Merging Fields of Computational Intelligence and Sensor Technology
Pages35-41
Number of pages7
DOIs
Publication statusPublished - 2011 Aug 10
EventSymposium Series on Computational Intelligence, IEEE SSCI 2011 - 2011 IEEE Workshop on Merging Fields of Computational Intelligence and Sensor Technology, CompSens 2011 - Paris, France
Duration: 2011 Apr 112011 Apr 15

Publication series

NameIEEE SSCI 2011 - Symposium Series on Computational Intelligence - CompSens 2011: 2011 IEEE Workshop on Merging Fields of Computational Intelligence and Sensor Technology

Other

OtherSymposium Series on Computational Intelligence, IEEE SSCI 2011 - 2011 IEEE Workshop on Merging Fields of Computational Intelligence and Sensor Technology, CompSens 2011
CountryFrance
CityParis
Period11/4/1111/4/15

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Intelligent sensing of biomedical signals - Lung tumor motion prediction for accurate radiotherapy'. Together they form a unique fingerprint.

  • Cite this

    Ichiji, K., Homma, N., Bukovsky, I., & Yoshizawa, M. (2011). Intelligent sensing of biomedical signals - Lung tumor motion prediction for accurate radiotherapy. In IEEE SSCI 2011 - Symposium Series on Computational Intelligence - CompSens 2011: 2011 IEEE Workshop on Merging Fields of Computational Intelligence and Sensor Technology (pp. 35-41). [5949518] (IEEE SSCI 2011 - Symposium Series on Computational Intelligence - CompSens 2011: 2011 IEEE Workshop on Merging Fields of Computational Intelligence and Sensor Technology). https://doi.org/10.1109/MFCIST.2011.5949518