@inproceedings{798ea1f75c0a4d3e9c009f706174f792,
title = "Characterization of physiological networks in sleep apnea patients using artificial neural networks for Granger causality computation",
abstract = "Different studies have used Transfer Entropy (TE) and Granger Causality (GC) computation to quantify interconnection between physiological systems. These methods have disadvantages in parametrization and availability in analytic formulas to evaluate the significance of the results. Other inconvenience is related with the assumptions in the distribution of the models generated from the data. In this document, the authors present a way to measure the causality that connect the Central Nervous System (CNS) and the Cardiac System (CS) in people diagnosed with obstructive sleep apnea syndrome (OSA) before and during treatment with continuous positive air pressure (CPAP). For this purpose, artificial neural networks were used to obtain models for GC computation, based on time series of normalized powers calculated from electrocardiography (EKG) and electroencephalography (EEG) signals recorded in polysomnography (PSG) studies.",
author = "Jhon C{\'a}rdenas and Orjuela-Ca{\~n}{\'o}n, {Alvaro D.} and Alexander Cerquera and Antonio Ravelo",
note = "Publisher Copyright: {\textcopyright} 2017 SPIE.; 13th International Conference on Medical Information Processing and Analysis, SIPAIM 2017 ; Conference date: 05-10-2017 Through 07-10-2017",
year = "2017",
doi = "10.1117/12.2284957",
language = "English (US)",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Natasha Lepore and Jorge Brieva and Garcia, {Juan David} and Eduardo Romero",
booktitle = "13th International Conference on Medical Information Processing and Analysis",
address = "United States",
}