We wish to report on a serendipitous technical observation regarding the calculation of the Granger causality between electroencephalogram (EEG) channels in subjects given propofol, as reported by Pullon et al.1 From subsequent application of this analysis to a separate patient dataset in 2022, it became apparent that there is no accepted convention for the sign of the autoregressive coefficients returned by the “armorf.m” MATLAB function; this means that the transfer function we used in the original analysis had been derived from sign-reversed coefficients. This letter compares the results from the originally published sign-reversed Granger analysis with the “corrected” orthodox Granger analysis methods for the Pullon dataset. The first stage of the Granger algorithm derives the auto- and cross-regression coefficients; thus, the sign reversal alters the subsequent transformation to the frequency domain, effectively acting like a complicated filter. Further mathematical investigations into this are underway, but, empirically, the sign-reversal...
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Correspondence|
March 2023
Sign-reversed versus Orthodox Granger Causality Analysis of the Electroencephalogram in General Anesthesia
Jamie W. Sleigh, M.D.;
Jamie W. Sleigh, M.D.
1University of Auckland, Auckland, New Zealand (J.S.) Jamie.sleigh@waikatodhb.health.nz
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Accepted for publication January 12, 2023.
Anesthesiology Newly Published on March 2023. doi:
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Jamie W. Sleigh, Rebecca M. Pullon; Sign-reversed versus Orthodox Granger Causality Analysis of the Electroencephalogram in General Anesthesia. Anesthesiology Newly Published on March 1, 2023. doi: https://doi.org/10.1097/ALN.0000000000004511
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