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.
Keywords
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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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DOI: https://doi.org/10.5176/2251-1911_CMCGS14.50_23
Publisher Name: Springer, Cham
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