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

doi:10.1109/NER49283.2021.9441375

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/Inform › Conference contribution

2 Scopus citations

Abstract

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.

Translated title of the contribution	Análisis de las redes fisiológicas del cerebro y el corazón mediante redes neuronales causalidad de granger
Original language	English (US)
Title of host publication	2021 10th International IEEE/EMBS Conference on Neural Engineering, NER 2021
Publisher	IEEE Computer Society
Pages	469-472
Number of pages	4
ISBN (Electronic)	9781728143378
DOIs	https://doi.org/10.1109/NER49283.2021.9441375
State	Published - May 4 2021
Event	10th International IEEE/EMBS Conference on Neural Engineering, NER 2021 - Virtual, Online, Italy Duration: May 4 2021 → May 6 2021

Publication series

Name	International IEEE/EMBS Conference on Neural Engineering, NER
Volume	2021-May
ISSN (Print)	1948-3546
ISSN (Electronic)	1948-3554

Conference

Conference	10th International IEEE/EMBS Conference on Neural Engineering, NER 2021
Country/Territory	Italy
City	Virtual, Online
Period	5/4/21 → 5/6/21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Mechanical Engineering

Access to Document

10.1109/NER49283.2021.9441375

Cite this

Jaimes-Albarracin, A. D., Orjuela-Canon, A. D., Jutinico, A. L., Bazurto, M. A., & Duenas, E. (2021). Brain and heart physiological networks analysis employing neural networks granger causality. In 2021 10th International IEEE/EMBS Conference on Neural Engineering, NER 2021 (pp. 469-472). Article 9441375 (International IEEE/EMBS Conference on Neural Engineering, NER; Vol. 2021-May). IEEE Computer Society. https://doi.org/10.1109/NER49283.2021.9441375

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title = "Brain and heart physiological networks analysis employing neural networks granger causality",

abstract = "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. ",

author = "Jaimes-Albarracin, \{Anggie D.\} and Orjuela-Canon, \{Alvaro D.\} and Jutinico, \{Andres L.\} and Bazurto, \{Maria A.\} and Elida Duenas",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 10th International IEEE/EMBS Conference on Neural Engineering, NER 2021 ; Conference date: 04-05-2021 Through 06-05-2021",

year = "2021",

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doi = "10.1109/NER49283.2021.9441375",

language = "English (US)",

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}

Jaimes-Albarracin, AD, Orjuela-Canon, AD, Jutinico, AL, Bazurto, MA & Duenas, E 2021, Brain and heart physiological networks analysis employing neural networks granger causality. in 2021 10th International IEEE/EMBS Conference on Neural Engineering, NER 2021., 9441375, International IEEE/EMBS Conference on Neural Engineering, NER, vol. 2021-May, IEEE Computer Society, pp. 469-472, 10th International IEEE/EMBS Conference on Neural Engineering, NER 2021, Virtual, Online, Italy, 5/4/21. https://doi.org/10.1109/NER49283.2021.9441375

Brain and heart physiological networks analysis employing neural networks granger causality. / Jaimes-Albarracin, Anggie D.; Orjuela-Canon, Alvaro D.; Jutinico, Andres L. et al.
2021 10th International IEEE/EMBS Conference on Neural Engineering, NER 2021. IEEE Computer Society, 2021. p. 469-472 9441375 (International IEEE/EMBS Conference on Neural Engineering, NER; Vol. 2021-May).

Research output: Chapter in Book/Inform › Conference contribution

TY - GEN

T1 - Brain and heart physiological networks analysis employing neural networks granger causality

AU - Jaimes-Albarracin, Anggie D.

AU - Orjuela-Canon, Alvaro D.

AU - Jutinico, Andres L.

AU - Bazurto, Maria A.

AU - Duenas, Elida

PY - 2021/5/4

Y1 - 2021/5/4

N2 - 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.

AB - 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.

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M3 - Conference contribution

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BT - 2021 10th International IEEE/EMBS Conference on Neural Engineering, NER 2021

PB - IEEE Computer Society

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ER -

Jaimes-Albarracin AD, Orjuela-Canon AD, Jutinico AL, Bazurto MA, Duenas E. Brain and heart physiological networks analysis employing neural networks granger causality. In 2021 10th International IEEE/EMBS Conference on Neural Engineering, NER 2021. IEEE Computer Society. 2021. p. 469-472. 9441375. (International IEEE/EMBS Conference on Neural Engineering, NER). doi: 10.1109/NER49283.2021.9441375

Brain and heart physiological networks analysis employing neural networks granger causality

Abstract

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UN SDGs

All Science Journal Classification (ASJC) codes

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