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The Granger Causality Effect between Cardiorespiratory Hemodynamic Signals

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Forging Connections between Computational Mathematics and Computational Geometry

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 124))

Abstract

Granger causality (GC) is one of the most popular measures to reveal causality influence of time series based on the estimated linear regression model and has been widely applied in economics and neuroscience due to its reliability, clarity, and robustness.

Granger causality has recently received increasing attention to study causal interactions of neurophysiological data; in this chapter we have developed a model of causality between the respiratory, hemodynamic, and cardiac signals, more specifically, a study based on the Granger causality between three ECG leads, blood pressure, central venous pressure, pulmonary arterial pressure, respiratory impedance, and airway CO2. We selected 187 patients of 250 for our study, taken from Montreal General Hospital/MF (Massachusetts General Hospital/Marquette Foundation) databases. These signals are ideal for understanding causality and coupling (unidirectional or bidirectional).

In this approach we aim to analyze and understand the interactions between the signals mentioned above, and identify the significance of this interaction. The originality of this chapter is the number of variables selected for the study. Unlike the majority of studies that are conducted only with two variables, our study is multidimensional. The main advantage of a multidimensional and multivariable model is to solve a myriad of problems which is not the case in the two-dimensional studies.

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Author information

Authors and Affiliations

  1. Faculty of Engineering Sciences, STIC Lab, University of Tlemcen, Tlemcen, Algeria

    Samir Ghouali & Mohammed Feham

  2. Faculty of Economics, Business and Management Sciences, POLDEVA Lab, University of Tlemcen, Tlemcen, Algeria

    Yassine Zakarya Ghouali

Authors
  1. Samir Ghouali
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  2. Mohammed Feham
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  3. Yassine Zakarya Ghouali
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Corresponding author

Correspondence to Samir Ghouali .

Editor information

Editors and Affiliations

  1. Department of Mathematical Sciences, The University of Liverpool, Liverpool, UK

    Ke Chen

  2. Global Science and Technology Forum, Singapore, Singapore

    Anton Ravindran (Managing Editor, President) (Managing Editor, President)

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Ghouali, S., Feham, M., Ghouali, Y.Z. (2016). The Granger Causality Effect between Cardiorespiratory Hemodynamic Signals. In: Chen, K., Ravindran, A. (eds) Forging Connections between Computational Mathematics and Computational Geometry. Springer Proceedings in Mathematics & Statistics, vol 124. Springer, Cham. https://doi.org/10.5176/2251-1911_CMCGS14.50_23

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