Tutorial on Multivariate Autoregressive Modelling
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...
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| Published in | Journal of clinical monitoring and computing Vol. 20; no. 2; pp. 101 - 108 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Dordrecht
Springer
01.04.2006
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1387-1307 1573-2614 |
| DOI | 10.1007/s10877-006-9013-4 |
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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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| AbstractList | 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. 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.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. |
| Author | Takalo, Reijo Hytti, Heli Ihalainen, Heimo |
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| Cites_doi | 10.1007/BF02520074 10.1109/10.477696 10.1007/s10877-005-7089-x 10.1161/01.HYP.10.5.538 10.1093/biomet/50.1-2.129 10.1080/18811248.1981.9733328 10.1615/CritRevBiomedEng.v33.i4.20 10.1029/JZ070i008p01885 10.1097/01.hjh.0000125469.35523.32 10.1016/0169-2607(96)01764-6 10.1002/sapm1946251261 10.1038/233339a0 10.2307/1401322 10.1016/0169-2607(96)01767-1 10.1016/0306-4549(95)00027-5 10.1109/IECON.1988.665158 10.1109/TAC.1974.1100705 |
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| Keywords | Intensive care Modeling multivariate autoregressive modelling cardiovascular dynamics Resuscitation |
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| References | R Takalo (9013_CR1) 2005; 19 9013_CR9 9013_CR8 R Oguma (9013_CR20) 1981; 18 I Korhonen (9013_CR6) 1996; 51 9013_CR25 H Akaike (9013_CR16) 1974; 19 9013_CR26 M Kaminski (9013_CR5) 2005; 33 I Korhonen (9013_CR7) 1996; 51 9013_CR21 9013_CR23 9013_CR24 G Parati (9013_CR27) 2004; 22 R Oguma (9013_CR3) 1996; 23 9013_CR14 9013_CR17 9013_CR18 9013_CR19 I Korhonen (9013_CR22) 1996; 34 9013_CR4 9013_CR2 9013_CR10 R Wiggins (9013_CR15) 1965; 70 9013_CR11 9013_CR12 9013_CR13 15201539 - J Hypertens. 2004 Jul;22(7):1259-63 4940430 - Nature. 1971 Oct 1;233(5318):339-41 16437291 - J Clin Monit Comput. 2005 Dec;19(6):401-10 8762826 - Med Biol Eng Comput. 1996 May;34(3):199-206 8894393 - Comput Methods Programs Biomed. 1996 Oct;51(1-2):85-94 8894396 - Comput Methods Programs Biomed. 1996 Oct;51(1-2):121-30 3942570 - Aviat Space Environ Med. 1986 Jan;57(1):49-53 15982186 - Crit Rev Biomed Eng. 2005;33(4):347-430 8567000 - IEEE Trans Biomed Eng. 1996 Jan;43(1):1-14 3666866 - Hypertension. 1987 Nov;10(5):538-43 |
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| SubjectTerms | Anesthesia. Intensive care medicine. Transfusions. Cell therapy and gene therapy Biological and medical sciences Biomedical Engineering - methods Blood Pressure Cardiovascular System - pathology Hematologic and hematopoietic diseases Humans Intensive care medicine Medical sciences Models, Cardiovascular Models, Statistical Models, Theoretical Multivariate Analysis Platelet diseases and coagulopathies Pneumology Pulmonary hypertension. Acute cor pulmonale. Pulmonary embolism. Pulmonary vascular diseases Regression Analysis Respiration Signal Processing, Computer-Assisted Systole |
| Title | Tutorial on Multivariate Autoregressive Modelling |
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