Tutorial on multivariate autoregressive modelling.

Heli Hytti; Reijo Takalo; Heimo Ihalainen

doi:10.1007/s10877-006-9013-4

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Tutorial on multivariate autoregressive modelling.

Heli Hytti·Reijo Takalo·Heimo Ihalainen

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Abstract

In the present paper, the theoretical background of multivariate autoregressive modelling (MAR) is explained. The motivation for MAR modelling is the need to study the linear relationships between signals. In biomedical engineering, MAR modelling is used especially in the analysis of cardiovascular dynamics and electroencephalographic signals, because it allows determination of physiologically relevant connections between the measured signals. In a MAR model, the value of each variable at each time instance is predicted from the values of the same series and those of all other time series. The number of past values used is called the model order. Because of the inter-signal connections, a MAR model can describe causality, delays, closed-loop effects and simultaneous phenomena. To provide a better insight into the subject matter, MAR modelling is here illustrated with a model between systolic blood pressure, RR interval and instantaneous lung volume.

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Biomedical Engineering Blood Pressure Cardiovascular System Humans Models, Cardiovascular Models, Statistical Models, Theoretical Multivariate Analysis Regression Analysis Respiration Signal Processing, Computer-Assisted Systole Journal Article

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Heli Hytti, Reijo Takalo, Heimo Ihalainen. 2006-05-16. Tutorial on multivariate autoregressive modelling.. https://doi.org/10.1007/s10877-006-9013-4

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