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Linear multivariate models for physiological signal analysis: theory

I Korhonen1, L Mainardi, P Loula

  • 1VIT Information Technology, Multimedia Systems, Tampere, Finland.

Computer Methods and Programs in Biomedicine
|October 1, 1996
PubMed
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A new general linear parametric multivariate model is introduced, enabling partial spectral analysis. This flexible model relaxes strict causality requirements for practical applications in system identification.

Area of Science:

  • Statistics
  • Systems Engineering
  • Signal Processing

Background:

  • Existing multivariate linear models often have limitations in practical applications.
  • Strict causality assumptions can hinder the utility of complex system modeling.

Purpose of the Study:

  • To present a general linear parametric multivariate modeling concept.
  • To derive partial spectral analysis from this general model.
  • To explore the relaxation of strict causality for practical system identification.

Main Methods:

  • Development of a general linear parametric multivariate model.
  • Derivation of partial spectral analysis.
  • Detailed description of multivariate autoregressive and dynamic adjustment models.
  • Consideration of time-varying modeling.

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Main Results:

  • The general model integrates various multivariate linear models.
  • Partial spectral analysis is derived from the general framework.
  • Abandoning strictly-causal structures enhances practical applicability.
  • Two sub-class models (autoregressive, dynamic adjustment) are detailed.

Conclusions:

  • The presented general multivariate model offers a flexible framework for system identification.
  • Relaxing strict causality is crucial for applying these models to real-world systems.
  • The methods are applicable to physiological data, as shown in companion work.