Brain and heart physiological networks analysis employing neural networks granger causality

This paper presents the study of brain-heart interactions in pediatric patients diagnosed with obstructive sleep apnea (OSA). A comparison between pre- and post- treatment stages was treated, searching to analyze the therapy effect. For this purpose, polysomnography signals were characterized, making use of electroencephalography and electrocardiography to compute the heart rate variability. Physiological networks analysis was driven through the computation of Granger causality to find interactions among brain and heart in both directions. Also, a proposal based on artificial neural networks was employed to include a nonlinear Granger causality sense. A preprocessing was implemented, according to five spectral subbands for the brain case, associate to five rhythms, and three for the heart case. Results showed that the treatment allowed recovery of connections mainly in subsystems involving the delta and gamma subbands. Finally, a notorious difference was evidenced between the results obtained with both methods where the nonlinear analysis obtained complementary results.