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Related Concept Videos

Signal and System01:26

Signal and System

A signal x(t) is a set of data or a time function representing a variable of interest. Signals typically convey information about a phenomenon, such as atmospheric temperature, humidity, human voice, television images, a dog's bark, or birdsongs. More generally, a signal can be a function of more than one independent variable. For instance, images depend on horizontal and vertical positions and can be regarded as two-dimensional signals. However, this text will focus on one-dimensional signals...

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Related Experiment Video

Updated: May 18, 2026

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
11:54

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Published on: May 8, 2021

System identification of physiological systems using short data segments.

Daniel Ludvig1, Eric J Perreault

  • 1Sensory Motor Performance Program, Rehabilitation Institute of Chicago, IL 60611, USA. daniel.ludvig@mail.mcgill.ca

IEEE Transactions on Bio-Medical Engineering
|October 4, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a new algorithm for system identification in physiology. It effectively estimates time-varying physiological systems using limited data, reducing variability and error compared to existing methods.

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Area of Science:

  • Physiological systems analysis
  • Biomedical engineering
  • Computational biology

Background:

  • System identification of physiological systems is challenging due to uncertain structures.
  • Nonparametric techniques often require stationarity and large datasets, which are difficult to obtain in physiological studies.
  • Existing ensemble methods for time-varying estimation need substantial data, limiting their applicability.

Purpose of the Study:

  • To develop a novel algorithm for system identification in physiological systems.
  • To address limitations of existing methods regarding data requirements and stationarity assumptions.
  • To improve the accuracy and efficiency of identifying time-varying physiological systems.

Main Methods:

  • Developed a novel algorithm utilizing multiple short data segments for system identification.
  • Employed simulation studies to evaluate the algorithm's performance.
  • Compared the new algorithm against previous nonparametric and ensemble methods.

Main Results:

  • The novel algorithm produced system estimates with lower variability when limited data was available.
  • Simulation studies demonstrated superior performance compared to existing methods under data constraints.
  • The algorithm generated time-varying system estimates with lower total error than ensemble methods.

Conclusions:

  • The developed algorithm is well-suited for identifying physiological systems that exhibit temporal variations.
  • It is particularly effective when only short segments of stationary data can be collected.
  • This method offers a viable solution for system identification in challenging physiological research settings.