Characterization of physiological networks in sleep apnea patients using artificial neural networks for Granger causality computation

Título:	Characterization of physiological networks in sleep apnea patients using artificial neural networks for Granger causality computation
Autores/as:	Cárdenas, J. Orjuela-Cañón, A. Cerquera, A. Ravelo García, Antonio G.
Clasificación UNESCO:	3314 Tecnología médica 120304 Inteligencia artificial
Palabras clave:	Artificial neural network Granger causality Obstructive sleep apnea syndrome Brain-heart networks CPAP
Fecha de publicación:	2017
Publicación seriada:	Proceedings of SPIE - The International Society for Optical Engineering
Conferencia:	13th International Conference on Medical Information Processing and Analysis, SIPAIM 2017
Resumen:	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.
Descripción:	13th International Conference on Medical Information Processing and Analysis, SIPAIM 2017; San Andres Island; Colombia; 5-7 October 2017; Code 132645
URI:	http://hdl.handle.net/10553/25108
ISBN:	9781510616332
ISSN:	0277-786X
DOI:	10.1117/12.2284957
Fuente:	Proceedings of SPIE - The International Society for Optical Engineering [ISSN 0277-786X] v. 10572, article number 1057219
Derechos:	by-nc-nd
Colección:	Artículos

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