System identification of physiological systems using short data segments

Daniel Ludvig*, Eric J. Perreault

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

32 Scopus citations


System identification of physiological systems poses unique challenges, especially when the structure of the system under study is uncertain. Nonparametric techniques can be useful for identifying system structure, but these typically assume stationarity and require large amounts of data. Both of these requirements are often not easily obtained in the study of physiological systems. Ensemble methods for time-varying nonparametric estimation have been developed to address the issue of stationarity, but these require an amount of data that can be prohibitive for many experimental systems. To address this issue, we developed a novel algorithm that uses multiple short data segments. Using simulation studies, we showed that this algorithm produces system estimates with lower variability than previous methods when limited data are present. Furthermore, we showed that the new algorithm generates time-varying system estimates with lower total error than an ensemble method. Thus, this algorithm is well suited for the identification of physiological systems that vary with time or from which only short segments of stationary data can be collected.

Original languageEnglish (US)
Article number6316159
Pages (from-to)3541-3549
Number of pages9
JournalIEEE Transactions on Biomedical Engineering
Issue number12
StatePublished - 2012


  • Biological system modeling
  • system identification
  • time-varying systems

ASJC Scopus subject areas

  • Biomedical Engineering


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