Brain and heart physiological networks analysis employing neural networks granger causality

Anggie D. Jaimes-Albarracin, Alvaro D. Orjuela-Canon, Andres L. Jutinico, Maria A. Bazurto, Elida Duenas

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish (US)
Title of host publication2021 10th International IEEE/EMBS Conference on Neural Engineering, NER 2021
PublisherIEEE Computer Society
Number of pages4
ISBN (Electronic)9781728143378
StatePublished - May 4 2021
Event10th International IEEE/EMBS Conference on Neural Engineering, NER 2021 - Virtual, Online, Italy
Duration: May 4 2021May 6 2021

Publication series

NameInternational IEEE/EMBS Conference on Neural Engineering, NER
ISSN (Print)1948-3546
ISSN (Electronic)1948-3554


Conference10th International IEEE/EMBS Conference on Neural Engineering, NER 2021
CityVirtual, Online

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Mechanical Engineering

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