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Proceedings Paper

Characterization of physiological networks in sleep apnea patients using artificial neural networks for Granger causality computation
Author(s): Jhon Cárdenas; Alvaro D. Orjuela-Cañón; Alexander Cerquera; Antonio Ravelo
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Paper 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.

Paper Details

Date Published: 17 November 2017
PDF: 7 pages
Proc. SPIE 10572, 13th International Conference on Medical Information Processing and Analysis, 1057219 (17 November 2017); doi: 10.1117/12.2284957
Show Author Affiliations
Jhon Cárdenas, Univ. Antonio Nariño (Colombia)
Alvaro D. Orjuela-Cañón, Univ. Antonio Nariño (Colombia)
Alexander Cerquera, Univ. of Florida (United States)
Antonio Ravelo, Univ. de Gran Canaria (Spain)

Published in SPIE Proceedings Vol. 10572:
13th International Conference on Medical Information Processing and Analysis
Eduardo Romero; Natasha Lepore; Jorge Brieva; Juan David García, Editor(s)

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